DEEP-IDS A REAL-TIME INTRUSION DETECTOR

FOR IOT NODES USING DEEP LEARNING

A PROJECT REPORT

Submitted by

MOWLEESWARAN V
SENTHAMIZHSELVAN M

in partial fulfilment for the award of the degree

of

BACHELOR OF ENGINEERING
IN

COMPUTER SCIENCE AND ENGINEERING

CHENNAI INSTITUTE OF TECHNOLOGY

(An Autonomous Institution, Affiliated to Anna University, Chennai)

ANNA UNIVERSITY: CHENNAI-600 025

MARCH 2025

1
 CHENNAI INSTITUTE OF TECHNOLOGY
(An Autonomous Institution, Affiliated to Anna University ,Chennai)
CHENNAI-600 069

BONAFIDE CERTIFICATE

Certified that this project report “DEEP-IDS A REAL-TIME INTRUSION

DETECTOR FOR IOT NODES USING DEEP LEARNING” is the Bonafide
work of “MOWLEESWARAN V(210421104301), SENTHAMZH SELVAN
M(210421104143)” who carried out the project work under my supervision

SIGNATURE SIGNATURE

Dr.S.PAVITHRA M.E,Ph.D. Dr.N.KANDAVEL M.E,Ph.D.

HEAD OF THE DEPARTMENT SUPERVISOR
Professor Asst.Professor
Department of Computer Science and Department Computer Science and
Engineering, Engineering,
Chennai Institute of Technology, Chennai Institue of Technology,
Kundrathur, Chennai-600069. Kundrathur, Chennai-600069

Certified that the above students have attended viva voice during the exam held
on ………………........

INTERNAL EXAMINER EXTERNALEXAMER

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 ACKNOWLEDGEMENT

We express our gratitude to our Chairman Shri.P.SRIRAM and all trust

members of Chennai Institute of Technology for providing the facility and
opportunity to do this project as a part of our undergraduate course.

We are grateful to our Principal Dr.A.RAMESH M.E, Ph.D. for providing

us the facility and encouragement during the course of our work.

We sincerely thank our Head of the Department, Dr.S.PAVITHRA M.E,Ph.D

Department of Computer Science and Engineering for having provided us
valuable guidance, resources and timely suggestions throughout our work.

We sincerely thank our Project Guide, Dr.N.KANDAVEL M.E,Ph.D Asst.

Professor, Department of Computer Science and Engineering for having
provided us with valuable guidance, resources and timely suggestions
throughout our work.

We would like to extend our thanks to our Faculty coordinator Ms.BHAVANI

RAMESH M. E , MBA (PhD) of the Department of Computer Science and
Engineering, for their valuable suggestions throughout this project.

We wish to extend our sincere thanks to all Faculty members of the

Department of Computer Science and Engineering for their valuable
suggestions and their kind cooperation for the successful completion of our
project.We wish to acknowledge the help received from the Lab Instructors of the
Department of Computer Science and Engineering and others for providing
valuable suggestions and for the successful completion of the project .

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 Abstract

The rapid growth of Internet of Things (IoT) devices has revolutionized various
industries, enhancing connectivity and automation. However, the increasing
number of IoT deployments also introduces significant cybersecurity challenges,
making them vulnerable to sophisticated cyber threats. Traditional Intrusion
Detection Systems (IDS) struggle to adapt to evolving attack patterns due to high
false positive rates and limited scalability. This project presents Deep-IDS, a
real-time intrusion detection system specifically designed for IoT environments
using deep learning techniques. By leveraging the NSL-KDD dataset, a deep
learning model is trained to effectively identify and mitigate cyber threats. The
proposed system employs advanced neural network architectures such as
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks
(RNNs) to improve detection accuracy. The implementation is deployed as a
Flask web application, ensuring scalability, real-time monitoring, and user-
friendly interaction. Deep-IDS follows a structured approach, including data
collection, preprocessing, feature selection, deep learning model training, and
real-time classification of intrusions. The system demonstrates high accuracy
with reduced false positives, making it an effective security solution for IoT
networks. Furthermore, its real-time detection capability enables rapid responses
to potential threats, enhancing overall cybersecurity. The findings of this project
highlight the efficiency of deep learning-based IDS in IoT security. Future
improvements include integrating federated learning for distributed IDS and
leveraging blockchain for secure logging. Deep-IDS serves as a robust and
intelligent security framework, safeguarding IoT ecosystems against emerging
cyber threats.

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 TABLE OF CONTENTS
Pag
Chapters Section Title e
No.
Abstract 4
Chapter
Introduction 6
1
1.1 Background 6
Cyber
Threats in
1.2 IoT 7
Environment
s
Limitations
of Traditional
1.3 8
Security
Mechanisms
The Role of
Deep
1.4 9
Learning in
IoT Security
Problem
1.5 10
Statement

Objectives of
1.6 11
the Study

Significance
1.7 12
of the Study

Scope of the
1.8 13
Project

Organization
1.9 14
of the Report

Chapter Literature
15
2 Review
2.1 Introduction 15

5
 Overview of
Intrusion
2.2 Detection 16
Systems
(IDS)
Traditional
2.3 IDS for IoT 17
Security

Machine
Learning-
2.4 Based 18
Intrusion
Detection
Deep
Learning for
2.5 19
Intrusion
Detection
Existing
Datasets for
2.6 20
Intrusion
Detection
Challenges
2.7 and Research 21
Gaps
2.8 Summary 22
Chapter
System Design 23
3
3.1 Introduction 23

System
3.2 24
Architecture
Block
3.3 25
Diagram
Module
3.4 26
Descriptions

6
 Data
3.4.1 Collection 27
Module

Data
3.4.2 Preprocessin 28
g Module

Feature
3.4.3 Selection 29
Module
CNN-Based
Deep
3.4.4 30
Learning
Model
Detection
3.4.5 31
System
Web-Based
3.4.6 User 33
Interface
Workflow of
3.5 34
Deep-IDS

3.6 Summary 34
Chapter Implementatio
35
4 n
4.1 Dataset Used 36

Data
4.2 Preprocessin 37
g Techniques
Deep
Learning
4.3 38
Model
Development
System
4.4 39
Deployment
4.5 Summary 39

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 Chapter
Source Code 40
5
Code for
Model
5.1 41
Building and
Predictions

Code for
5.2 42
Deployment

Experimental
Chapter
Results and 43
6
Analysis

6.1 Introduction 43

Model
6.2 Evaluation 44
and Accuracy

Performance
6.3 45
Metrics
Comparison
with
6.4 46
Traditional
IDS
System
Performance
6.5 47
and Interface
Screenshots

6.6 Summary 48
Chapter Conclusion and
49
7 Future Work
7.1 Conclusion 49
7.2 Future Work 49
References 51

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 LIST OF FIGURES
TABLE NO. TITLE PAGE NO.

3.1 System Architecture of 24

Deep-IDS
3.2 Block diagram 25

3.3 CNN module 30

Architecture
3.4 Web application UI 32

6.1 Web application home 47

page
6.2 Confusion matrix 46

6.3 Modelling page 45

6.4 Prediction page 47

6.5 Performance page 48

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 CHAPTER I: INTRODUCTION

1.1 Background

The Internet of Things (IoT) has rapidly transformed various industries,

leading to increased automation, improved efficiency, and enhanced connectivity.
IoT devices are widely deployed in domains such as smart homes, healthcare,
industrial automation, transportation, agriculture, and smart cities, offering
innovative solutions that streamline operations and improve user experiences.
These devices, equipped with sensors and embedded communication systems,
facilitate seamless data exchange over networks, enabling real-time monitoring
and control of physical systems.

Despite its advantages, the interconnected nature of IoT introduces significant

security vulnerabilities. Unlike traditional computing devices such as personal
computers and servers, IoT nodes often have limited computational resources,
constrained memory, and low-power processing capabilities. These limitations
make it challenging to implement conventional cybersecurity mechanisms like
firewalls, antivirus software, and encryption protocols at the device level.
Furthermore, IoT networks frequently operate in heterogeneous environments,
where devices from different manufacturers with varying security configurations
coexist, creating additional security risks. Furthermore, Deep-IDS enhances
threat detection accuracy by minimizing false positives and identifying
previously unknown attack vectors. Unlike conventional signature-based IDS,
which relies on predefined attack patterns, Deep-IDS leverages machine learning
algorithms to recognize anomalies and evolving threats in real time. This
adaptability ensures that IoT networks remain secure even against zero-day
exploits and sophisticated cyber threats.

Additionally, Deep-IDS offers scalability, making it suitable for diverse IoT

environments, from small-scale smart home networks to large-scale industrial IoT

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deployments. Its ability to process vast amounts of network traffic efficiently
enables organizations to maintain security without compromising system
performance. By utilizing deep learning models, Deep-IDS continuously
improves its detection capabilities, ensuring long-term resilience against
emerging cyber threats.

Moreover, the implementation of Deep-IDS supports regulatory compliance

by helping organizations adhere to industry security standards and guidelines. As
governments and enterprises emphasize cybersecurity in IoT deployments,
having an intelligent IDS solution becomes a critical factor in risk management.

Beyond security, Deep-IDS contributes to operational efficiency by reducing

downtime caused by security breaches, thereby preventing financial losses and
reputational damage. By proactively detecting and mitigating attacks, businesses
and individuals can fully leverage the benefits of IoT technology without
compromising safety and privacy. The significance of this study lies in its
potential to bridge the security gap in IoT networks, fostering a safer and more
reliable digital ecosystem.

1.1.1 Cyber Threats in IoT Environments

The increasing reliance on IoT has made these systems attractive targets for
cybercriminals, who exploit vulnerabilities to launch attacks such as:

 Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS)

attacks – Attackers flood IoT devices with excessive requests, causing
network congestion and service disruptions.

 Malware and Ransomware – Malicious software is used to take control

of IoT nodes, steal data, or encrypt critical files for ransom.

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  Man-in-the-Middle (MITM) attacks – Cybercriminals intercept and
manipulate communications between IoT devices and servers,
compromising data integrity and confidentiality.

 Spoofing and Identity Theft – Attackers impersonate legitimate IoT

devices to gain unauthorized access to networks and sensitive information.

 Eavesdropping and Data Breaches – Unsecured communication

channels allow hackers to monitor IoT data transmissions and extract
confidential information.

1.1.2 Limitations of Traditional Security Mechanisms

Traditional Intrusion Detection Systems (IDS) are classified into two primary
types:

1. Signature-Based IDS:

 Detects threats by comparing incoming traffic patterns against a

database of known attack signatures.

 Effective for identifying previously documented attacks but fails

against new or unknown threats.

 Requires frequent updates to remain effective, making it less

adaptable to emerging cybersecurity challenges.

2. Anomaly-Based IDS:

 Identifies potential intrusions by detecting deviations from normal

system behavior.

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  Uses predefined behavioral baselines, but can generate high false
positive rates if benign activities are misclassified as threats.

 Struggles to adapt to dynamically evolving network conditions in

IoT environments.

Both approaches fail to provide real-time detection and mitigation of emerging

threats, especially in resource-constrained IoT networks. They also lack self-
learning capabilities, meaning they cannot autonomously improve their detection
performance based on new attack patterns.

1.1.3 The Role of Deep Learning in IoT Security

The rapid expansion of IoT networks has introduced significant security

challenges, as traditional intrusion detection systems (IDS) struggle to keep up
with evolving cyber threats. Deep learning has emerged as a powerful solution,
enhancing the ability of IDS to detect both known and unknown attacks with high
accuracy. Unlike conventional machine learning models that rely on handcrafted
features, deep learning algorithms, such as Convolutional Neural Networks
(CNNs) and Recurrent Neural Networks (RNNs), can automatically extract
complex patterns from network traffic data.

Deep learning-based IDS offers several advantages in IoT security. CNNs are
effective in analyzing spatial features of network traffic, while RNNs and Long
Short-Term Memory (LSTM) networks help detect temporal anomalies in
sequential data. Hybrid models combining these techniques improve overall
detection accuracy and adaptability. Additionally, autoencoders and Generative
Adversarial Networks (GANs) are widely used for anomaly detection, enabling
the identification of zero-day attacks.

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 Learn complex patterns from vast amounts of network traffic data.

 Identify both known and unknown threats without relying on

predefined signatures.

 Continuously adapt to evolving cyber threats by retraining models with

new attack datasets.

 Reduce false positives by improving the precision of anomaly detection.

Deep learning techniques such as Convolutional Neural Networks (CNNs),

Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM)
networks, and autoencoders have demonstrated significant potential in
classifying cyberattacks and improving IoT security. These models can efficiently
analyse network traffic patterns, detect anomalies in real-time, and provide
automated responses to mitigate security breaches.

1.2 Problem Statement

The existing security solutions for IoT networks suffer from several limitations:

 High False Positive Rates – Traditional IDS often misclassifies benign

activities as threats.

 Limited Adaptability – Signature-based IDS struggles to detect new and

evolving attack patterns.

 Resource Constraints – IoT devices have limited computational power,

making conventional security mechanisms inefficient.

 Lack of Real-Time Detection – Many IDS solutions rely on periodic

updates, failing to provide real-time threat detection and response.

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1.3 Objectives of the Study

This project aims to address these challenges by developing Deep-IDS, an

efficient, deep learning-based intrusion detection system tailored for IoT
environments. The key objectives include:

 Developing a real-time IDS using deep learning techniques.

 Training and evaluating the model using the NSL-KDD dataset to enhance
detection accuracy.

 Implementing the system as a scalable and user-friendly Flask web

application.

 Ensuring low false positive rates while detecting various cyber threats.

 Enhancing IoT security through adaptive and intelligent threat detection

mechanisms.

1.4 Significance of the Study

The growing number of cyberattacks on IoT networks highlights the urgency of

proactive security solutions. Deep-IDS provides a robust, intelligent, and scalable
approach to intrusion detection. By integrating deep learning, the system can
continuously learn from new attack patterns, making it more effective than
traditional IDS solutions. In contrast to conventional intrusion detection systems
that rely on static rule-based mechanisms, Deep-IDS utilizes advanced machine
learning techniques to analyze network traffic dynamically. This enables it to
detect even subtle deviations from normal behavior, allowing for early threat
detection and mitigation. Furthermore, its self-learning capability ensures that it
adapts to evolving cyber threats, including zero-day attacks and sophisticated
malware.

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Another significant advantage of Deep-IDS is its scalability. It can be deployed
across various IoT environments, ranging from small-scale smart homes to
complex industrial systems, without significant performance degradation. The
system efficiently handles large volumes of network data, ensuring real-time
threat analysis without introducing latency or excessive resource consumption.

Moreover, Deep-IDS enhances cybersecurity resilience by reducing false

positives and improving detection accuracy. Traditional IDS often generate high
rates of false alerts, leading to unnecessary administrative overhead. However,
Deep-IDS leverages deep learning models to distinguish between benign and
malicious activities more accurately, minimizing the burden on security teams.

Additionally, the adoption of Deep-IDS aligns with global cybersecurity

regulations and industry standards, helping organizations meet compliance
requirements. With the increasing emphasis on data privacy and security
frameworks, implementing an intelligent IDS system strengthens an
organization’s risk management strategy.

Beyond security, Deep-IDS contributes to the stability and reliability of IoT

networks. By proactively identifying and mitigating security breaches, it helps
prevent downtime, financial losses, and reputational damage. This study is
significant as it provides an innovative and adaptive solution to IoT security
challenges, ensuring a safer and more resilient digital ecosystem for businesses
and individuals alike.

This research contributes to:

 Enhancing security in IoT environments.

 Reducing cybersecurity risks by detecting intrusions in real-time.

 Providing a scalable framework that can be deployed across diverse IoT

ecosystems.

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1.5 Scope of the Project

Deep-IDS is designed for real-time network traffic monitoring and intrusion

detection in IoT nodes. The project involves:

 Collecting and preprocessing network traffic data using the NSL-KDD

dataset.

 Training deep learning models (CNNs and RNNs) for accurate attack
detection.

 Deploying a Flask-based web application for real-time monitoring and

reporting.

 Validating the system’s performance in terms of accuracy, efficiency, and

scalability

1.6 Organization of the Report

This report is structured into several chapters, each detailing different aspects of
the Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep
Learning project. The contents of each chapter are summarized below:

 Chapter 1: Introduction – Provides an overview of the project, including

background information, problem statement, objectives, significance,
scope, and organization of the report.

 Chapter 2: Literature Review – Reviews existing research on intrusion

detection systems, deep learning techniques for cybersecurity, and IoT
security challenges.

 Chapter 3: System Design – Describes the proposed system architecture,

block diagram, module descriptions, and workflow of Deep-IDS.

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  Chapter 4: Implementation – Explains the dataset used, preprocessing
techniques, deep learning model development, and system deployment.

 Chapter 5: Experimental Results and Analysis – Presents the model

evaluation, accuracy, performance metrics, and comparison with
traditional IDS.

 Chapter 6: Conclusion and Future Work – Summarizes key findings,

project contributions, and potential enhancements for future
improvements.

CHAPTER II: LITERATURE SURVEY

2.1 Introduction

The increasing reliance on IoT devices in various industries has led to

significant security concerns. As IoT networks grow, they become attractive
targets for cybercriminals due to their limited security mechanisms and resource
constraints. Unlike traditional computing systems, many IoT devices operate with
minimal computational power, making it challenging to implement robust
security protocols. Additionally, their continuous connectivity and large-scale
deployments create numerous entry points for attackers, increasing the risk of
unauthorized access, data breaches, and service disruptions.

Traditional security solutions, such as firewalls and signature-based

intrusion detection systems, often fail to provide adequate protection against
evolving cyber threats. Attackers constantly develop new techniques, including
zero-day exploits, botnet-driven DDoS attacks, and AI-powered cyber intrusions,
which can bypass conventional defences. Furthermore, IoT networks frequently
consist of heterogeneous devices from multiple manufacturers, each with varying

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security configurations, making it difficult to implement standardized protection
measures.

This chapter presents a detailed survey of existing intrusion detection

mechanisms, deep learning approaches for cybersecurity, and the role of
advanced detection techniques in securing IoT environments. It explores how
traditional IDS solutions, such as anomaly-based and signature-based systems,
function and examines their strengths and weaknesses. Additionally, deep
learning-based IDS solutions are analyzed, highlighting their ability to detect
complex attack patterns and adapt to emerging threats in real time.

A review of past research studies provides insights into the limitations of

conventional methods and highlights the advantages of deep learning-based
intrusion detection. Several studies demonstrate how machine learning and deep
learning techniques, such as neural networks, autoencoders, and reinforcement
learning, can enhance threat detection accuracy and reduce false positives. These
approaches leverage vast amounts of network traffic data to identify suspicious
behaviors and unknown attack vectors more effectively than traditional models.

Moreover, the chapter discusses the integration of AI-driven security

measures with IoT ecosystems, emphasizing the potential of federated learning,
blockchain-based security frameworks, and edge computing for decentralized
threat detection. By understanding these advancements, researchers and
practitioners can develop more resilient and intelligent security solutions tailored
to the unique challenges of IoT networks. The findings from this survey aim to
bridge the security gap in IoT environments and pave the way for the next
generation of intrusion detection systems.

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2.2 Overview of Intrusion Detection Systems (IDS)

Intrusion detection systems play a crucial role in identifying malicious

activities within a network. They are generally categorized into signature-based
IDS and anomaly-based IDS. Signature-based IDS relies on predefined attack
patterns or signatures to detect intrusions. While this method is effective against
known threats, it struggles to detect novel attacks that do not have predefined
signatures. Anomaly-based IDS, on the other hand, monitors network behavior
and detects deviations from normal activity. This approach is more effective in
identifying zero-day attacks; however, it suffers from high false positive rates due
to the difficulty of defining a baseline for normal behavior.

Several intrusion detection frameworks have been developed for traditional IT

environments, but their direct application to IoT networks is challenging due to
the constraints of IoT devices, such as low computational power and limited
memory. The increasing sophistication of cyber threats demands intelligent and
adaptive IDS solutions capable of real-time detection and mitigation of attacks.

2.3 Traditional IDS for IoT Security

To address these limitations, researchers have turned to deep learning-based IDS

solutions. Convolutional Neural Networks (CNNs) and Recurrent Neural
Networks (RNNs) have been introduced to enhance detection accuracy by
automatically extracting meaningful features from network traffic. Unlike
traditional machine learning models that rely on manually selected features, deep
learning models can learn hierarchical patterns from raw data, improving their
adaptability to emerging cyber threats. CNNs are particularly effective in
capturing spatial correlations in network traffic, while RNNs, including Long
Short-Term Memory (LSTM) networks, are well-suited for analyzing sequential
data and detecting anomalies over time.

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Hybrid deep learning models combining multiple architectures have shown
promising results in detecting both known and unknown intrusions. For example,
combining CNNs with LSTMs can leverage spatial and temporal features
simultaneously, leading to improved classification accuracy. Autoencoders and
Generative Adversarial Networks (GANs) have also been explored for
unsupervised anomaly detection, where the model learns to differentiate between
normal and suspicious activities without relying on labeled attack data. These
techniques enable IDS solutions to detect zero-day attacks, making them more
effective in real-world IoT environments.

Recent advancements in federated learning and edge computing further improve

IDS deployment in resource-constrained IoT environments. Traditional deep
learning models require centralized data collection, which raises privacy concerns
and increases the risk of data breaches. Federated learning allows multiple
devices to collaboratively train a shared model without exchanging raw data,
preserving user privacy while enhancing model performance. Edge computing
reduces reliance on centralized servers by processing network traffic closer to the
data source, reducing latency and improving real-time detection capabilities.
These approaches ensure that IDS solutions can be deployed efficiently in IoT
networks, where bandwidth and computational resources are often limited.

Despite these improvements, several challenges remain in implementing deep

learning-based IDS solutions. High computational costs associated with training
deep models make it difficult to deploy them on low-power IoT devices.
Optimizing model architectures and using techniques like quantization and
pruning can help reduce resource consumption while maintaining accuracy.
Additionally, real-time processing constraints require IDS models to quickly
analyze vast amounts of network traffic without causing delays in system
performance. Developing lightweight yet powerful deep learning models is an
ongoing area of research to address this challenge.

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Another critical challenge is model interpretability. Many deep learning models
operate as "black-box" systems, making it difficult for security analysts to
understand their decision-making process. Enhancing explainability through
techniques like SHAP (SHapley Additive exPlanations) and LIME (Local
Interpretable Model-agnostic Explanations) can help improve trust and adoption
of deep learning-based IDS. The integration of blockchain for secure threat
intelligence sharing and reinforcement learning for adaptive detection strategies
are also emerging solutions that could further enhance the effectiveness of IDS in
IoT networks. By continuously refining these approaches, researchers can
develop more robust, efficient, and scalable IDS solutions capable of addressing
the evolving cybersecurity challenges in IoT environments.

2.4 Machine Learning-Based Intrusion Detection

Machine learning has been widely adopted in intrusion detection systems (IDS)
to enhance their ability to recognize sophisticated attack patterns. Supervised
learning techniques, such as decision trees, support vector machines (SVM), and
random forests, classify network traffic into normal and malicious categories.
These models require labeled datasets for training and rely on handcrafted feature
selection to optimize performance. Despite their effectiveness, traditional
machine learning models often struggle to adapt to new attack types, limiting their
applicability in real-world IoT networks.

To overcome these limitations, unsupervised learning techniques, such as

clustering and anomaly detection algorithms, have been explored. These methods
can detect unknown threats by identifying deviations from normal traffic patterns.
However, they are often prone to high false positives due to the lack of precise
attack labels during training. Semi-supervised approaches, which combine both

22
labeled and unlabeled data, have been introduced to improve detection
performance while reducing the dependency on extensive labeled datasets.

More recent advancements, such as deep learning-based approaches, leverage

neural networks to automatically extract complex features from network traffic,
eliminating the need for manual feature engineering. These models, particularly
CNNs and recurrent neural networks (RNNs), have demonstrated superior
accuracy and adaptability, making them a promising solution for securing
dynamic IoT environments against evolving cyber threats.

2.5 Deep Learning for Intrusion Detection

Deep learning has emerged as a powerful tool for intrusion detection due
to its ability to automatically extract meaningful features from large volumes of
network traffic data. Unlike traditional machine learning models that require
manual feature engineering, deep learning models can learn complex
representations from raw input data. Several neural network architectures,
including convolutional neural networks (CNNs), recurrent neural networks
(RNNs), and long short-term memory (LSTM) networks, have been successfully
applied to cybersecurity applications.

CNNs have been used for intrusion detection by analyzing network traffic as
image-like representations. These models can detect spatial patterns in network
data, improving classification accuracy. RNNs and LSTMs are particularly useful
for detecting attacks in time-series network traffic, as they can capture sequential
dependencies and identify anomalies over time. Autoencoders, a type of
unsupervised deep learning model, have been employed to detect novel intrusions
by reconstructing normal traffic patterns and flagging deviations as potential
threats.

23
The use of deep learning in intrusion detection has shown promising results, with
improved accuracy and reduced false positive rates compared to traditional
methods. However, the computational complexity of deep learning models poses
a challenge for deployment in resource-constrained IoT environments.
Researchers have proposed lightweight deep learning models and model
compression techniques to address these limitations, making deep IDS solutions
more feasible for IoT security applications.

2.6 Existing Datasets for Intrusion Detection

The effectiveness of intrusion detection models depends largely on the quality of

the datasets used for training and evaluation. High-quality datasets allow machine
learning models to learn complex attack patterns, differentiate between benign
and malicious activities, and improve detection accuracy. Several publicly
available datasets have been widely used in research to develop and benchmark
IDS solutions, providing a foundation for evaluating different security
techniques.

The NSL-KDD dataset is one of the most commonly used datasets for intrusion
detection, containing various attack types categorized into four major groups:
denial-of-service (DoS) attacks, probing attacks, remote-to-local (R2L) attacks,
and user-to-root (U2R) attacks. Despite its popularity, NSL-KDD has limitations,
such as outdated attack patterns that may not reflect modern cyber threats.
Additionally, it lacks encrypted traffic samples and real-time network behaviors,
making it less suitable for evaluating IDS models in contemporary IoT
environments.

Other datasets, such as CICIDS2017 and UNSW-NB15, provide more recent

network traffic data with realistic attack scenarios. These datasets include a
broader range of attack types and more representative normal traffic, making

24
them more suitable for evaluating modern IDS solutions. CICIDS2017 contains
traffic data generated from realistic attack simulations, such as brute-force
attacks, botnets, and web-based intrusions, making it useful for deep learning-
based IDS training. Similarly, UNSW-NB15 includes a combination of synthetic
and real-world traffic, offering a balanced dataset with multiple attack vectors.

The TON_IoT dataset has been specifically designed for IoT security research,
containing traffic data from IoT networks with labeled attack categories. Unlike
traditional datasets, TON_IoT captures device-specific vulnerabilities, making it
valuable for training IDS models in resource-constrained environments. It
includes network telemetry from IoT, industrial IoT (IIoT), and edge devices,
reflecting the increasing adoption of connected technologies in various sectors.

The availability of diverse datasets enables researchers to train and test deep
learning-based IDS models in different network environments. By leveraging
multiple datasets, security researchers can enhance model generalization,
ensuring that IDS solutions remain effective against previously unseen threats.
Furthermore, ongoing efforts to create new benchmark datasets with up-to-date
attack patterns, encrypted traffic analysis, and adversarial examples will play a
crucial role in advancing IDS capabilities. The integration of real-world traffic
with synthetic attack scenarios will further strengthen the reliability of IDS
models, enabling them to detect and mitigate evolving cyber threats in IoT
ecosystems effectively. Additionally, dataset augmentation techniques, such as
data synthesis and transformation, can help improve the model's robustness
against sophisticated attack variations. Collaborative research initiatives focusing
on open-source dataset sharing will also accelerate innovation in IDS
development.

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2.7 Challenges and Research Gaps

Despite the advancements in IDS technologies, several challenges remain in the

development of effective intrusion detection systems for IoT environments. One
of the major challenges is the adaptability of IDS models to evolving attack
patterns. Cyber threats are constantly changing, requiring IDS solutions that can
dynamically learn and update their detection capabilities without frequent manual
intervention. Another challenge is the trade-off between detection accuracy and
computational efficiency. Deep learning models, while powerful, require
significant processing resources, making it difficult to deploy them on IoT
devices with limited hardware capabilities.

Another research gap is the integration of IDS with real-time security response
mechanisms. Most IDS solutions focus on threat detection without providing
automated mitigation strategies. The combination of intrusion detection with
proactive security measures, such as intrusion prevention systems (IPS) and
blockchain-based security frameworks, is an area that requires further
exploration. Additionally, the development of explainable IDS models is crucial
to enhance trust and transparency in cybersecurity decision-making. Many deep
learning models operate as black-box systems, making it difficult for security
analysts to interpret their predictions.

Future research should focus on optimizing deep learning models for edge
computing, reducing dependency on cloud-based processing, and incorporating
federated learning to enhance privacy and security. Strengthening adversarial
robustness in IDS models will help defend against sophisticated evasion
techniques used by attackers. Furthermore, integrating IDS with threat
intelligence platforms can enhance detection accuracy by leveraging real-time
global threat data, while improving energy-efficient IDS models will facilitate
seamless deployment in resource-constrained IoT environments.

26
2.8 Summary

This chapter examined existing intrusion detection approaches, emphasizing the

limitations of traditional methods and the benefits of deep learning-based
solutions. Conventional IDS techniques, such as signature-based and anomaly-
based detection, struggle to keep up with evolving cyber threats due to their
reliance on predefined rules and static heuristics. In contrast, deep learning
models enhance detection accuracy by learning complex attack patterns and
adapting to new threats. However, challenges such as high computational costs,
real-time implementation, and scalability remain areas of ongoing research.

The effectiveness of IDS models is heavily dependent on the selection of

appropriate datasets. Many existing datasets contain outdated attack patterns or
lack real-world traffic diversity, limiting their applicability to modern
cybersecurity threats. Ensuring high-quality datasets with up-to-date attack
scenarios is essential for improving model robustness and performance.

Addressing these challenges through continuous research and technological

advancements is crucial for strengthening IoT security. Innovations in edge
computing and federated learning can help optimize IDS models for resource-
constrained IoT devices, reducing the reliance on centralized processing.

Additionally, explainable AI can improve transparency, enabling security

professionals to interpret and trust model decisions. By integrating these
advancements, IDS solutions can become more efficient, adaptive, and resilient,
offering enhanced protection for modern IoT environments against emerging
cyber threats.

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 CHAPTER III: SYSTEM DESIGN

3.1 Introduction

This chapter reviewed intrusion detection approaches, emphasizing the

limitations of traditional methods and the benefits of deep learning-based
solutions. While deep learning improves IDS accuracy by recognizing complex
attack patterns, challenges such as high computational costs, adaptability to new
threats, and real-time implementation remain areas of active research. Many deep
learning models require significant processing power, which can be a constraint
in resource-limited IoT environments. Additionally, adversarial attacks, where
attackers attempt to manipulate IDS models, pose an ongoing challenge in
ensuring robust cybersecurity.

The selection of appropriate datasets is crucial for training effective IDS models.
However, many existing datasets contain outdated attack patterns, lack real-world
traffic diversity, or suffer from class imbalances. Since cyber threats continue to
evolve, using high-quality, diverse datasets that accurately represent modern
attack scenarios is essential for improving IDS performance. Enhancing dataset
relevance and incorporating encrypted traffic analysis can further strengthen
detection capabilities and ensure models are prepared for emerging cyber threats.

Addressing these challenges through innovative research will significantly

enhance IoT security. Advancements in edge computing can help process IDS
data closer to the source, reducing latency and computational overhead. Federated
learning can improve model training across distributed IoT devices without
compromising data privacy. Additionally, explainable AI (XAI) techniques can
enhance transparency in IDS decision-making, making it easier for security
professionals to interpret and trust detection results.

By continuously refining IDS models and integrating emerging technologies,

researchers can develop more efficient and adaptable cybersecurity solutions.

28
These improvements will help secure IoT environments against sophisticated
attacks, ensuring a safer and more resilient digital ecosystem.

3.2 System Architecture

The Deep-IDS architecture is designed to effectively detect intrusions in IoT

environments by leveraging deep learning techniques. It follows a structured,
modular approach to process network traffic, extract meaningful features, and
classify network activity as either normal or malicious. By integrating a CNN-
based deep learning model, Deep-IDS enhances the accuracy of intrusion
detection while ensuring real-time security monitoring.

The system consists of six key components, each performing a critical role in
intrusion detection:

1. Data Collection Module

The data collection module is responsible for gathering network traffic data from
various sources, ensuring that the system has a diverse dataset for training and
real-time monitoring.

 Captures real-time network traffic from IoT devices, routers, and

connected nodes.

 Integrates publicly available datasets like NSL-KDD, CICIDS2017, and

UNSW-NB15 to train the model on diverse attack types.

 Collects key network parameters such as IP addresses, ports, protocols,

packet sizes, timestamps, and connection durations.

 Ensures real-time streaming of network traffic to continuously update

the system with the latest network behaviors and potential threats.

29
  2. Preprocessing Module

Before feeding the data into the deep learning model, preprocessing is crucial to
ensure data consistency, noise reduction, and proper formatting.

 Data Cleaning: Removes duplicate entries, irrelevant records, and

corrupted data points.

 Normalization: Scales numerical features (e.g., packet sizes, connection

durations) to a standard range to prevent bias toward larger values.

 Encoding: Converts categorical features such as protocol type, service

names, and attack labels into numerical representations using one-hot
encoding or label encoding.

 Handling Missing Values: Uses imputation methods like mean, median,

or mode to fill missing data or removes records with excessive missing
values.

By ensuring clean and well-structured input data, the preprocessing module

improves the accuracy and efficiency of the intrusion detection system.

3. Feature Selection Module

Not all network traffic features are relevant for intrusion detection. The feature
selection module extracts and selects the most significant features to improve
model efficiency and reduce computational overhead.

 Dimensionality Reduction: Uses algorithms like Principal Component

Analysis (PCA) and Recursive Feature Elimination (RFE) to remove
redundant or less informative features.

30
  Feature Engineering: Identifies critical network attributes such as source
and destination ports, protocol types, connection state, and traffic
patterns that contribute to detecting intrusions.

 Enhances Model Interpretability: By focusing on key features, the

module ensures that the deep learning model is trained with meaningful
data, reducing overfitting and improving generalization.

4. CNN-Based Deep Learning Model

The Convolutional Neural Network (CNN) is the core of Deep-IDS,

responsible for analyzing network traffic and classifying it as normal or
malicious. Unlike traditional machine learning models that rely on manual feature
extraction, CNN automatically learns hierarchical patterns from network
traffic data.

Model Components:

 Input Layer: Accepts structured network traffic data.

 Convolutional Layers: Apply filters to detect spatial relationships in

network traffic features.

 Pooling Layers: Reduce feature dimensions while retaining important

information, improving computational efficiency.

 Fully Connected Layers: Integrate extracted features to form a final

decision representation.

 Output Layer: Uses activation functions like Softmax (for multi-class

classification) or Sigmoid (for binary classification) to predict normal or
malicious traffic.

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Optimization & Training:

 Uses Adam optimizer to fine-tune model weights efficiently.

 Binary Cross-Entropy loss function ensures accurate classification

between normal and malicious traffic.

 Hyperparameter tuning is performed to adjust the learning rate, batch

size, and number of layers for optimal accuracy.

 The model is trained using a split dataset (training, validation, and test
sets) to prevent overfitting and evaluate performance on unseen data.

By utilizing deep learning, the model improves detection accuracy,

generalization to new attacks, and adaptability to evolving cyber threats.

Detection and Alert System

Once the CNN model detects a potential threat, the detection and alert system
ensures that security administrators are promptly notified.

 Real-Time Threat Detection: Monitors network traffic continuously,

classifying it as normal or malicious.

 Immediate Alerts: Generates alerts for detected intrusions, notifying

administrators via email, SMS, or dashboard notifications.

 False Positive Reduction: Uses additional filters and adaptive learning

techniques to minimize the number of false alarms.

 Threat Logging: Stores detected attacks in a secure database for further

analysis and improvement of the model.

This module helps ensure proactive security management by providing timely

alerts and insights into network security.

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Web-Based User Interface

Deep-IDS includes a web-based interface that allows security administrators to

monitor network activity in real time and analyze security incidents effectively.

Features of the Web Interface:

 Live Dashboard: Displays real-time traffic monitoring, showing network

activity, detected intrusions, and system performance.

 Graphical Analysis: Provides charts, confusion matrices, and

performance metrics such as accuracy, precision, recall, and F1-score.

 Hyperparameter Adjustment: Allows administrators to retrain the

model with different configurations to improve detection accuracy.

 Log Management: Maintains a history of detected threats for future

reference and forensic analysis.

By integrating a user-friendly interface, Deep-IDS simplifies network security

management, making it accessible even for administrators with limited deep
learning expertise.

The system architecture of Deep-IDS provides a robust, scalable, and efficient

solution for intrusion detection in IoT environments. By integrating deep learning
techniques with real-time monitoring and automated threat alerts, Deep-IDS
enhances network security against both known and emerging cyber threats.

The structured pipeline—from data collection and preprocessing to CNN-

based classification and web-based monitoring—ensures that the system
remains adaptable, accurate, and user-friendly. The inclusion of a detection
and alert system further strengthens its practical usability, making it an effective
intrusion detection solution for modern IoT networks

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 Figure 3.1 System Architecture of Deep-IDS

3.3 Block Diagram

The block diagram of Deep-IDS visually represents the flow of data and
processing steps within the system. It illustrates the core components, including
data input, preprocessing, feature extraction, model training, and threat
classification.

34
The system follows a sequential process:

 Network traffic data is collected from IoT nodes.

 The data undergoes preprocessing, including normalization and feature

extraction.

 A CNN-based deep learning model is trained to classify network traffic.

 The trained model is deployed for real-time detection and alerts.

Figure 3.2 Block Diagram of Deep-IDS

3.4 Module Descriptions

The Deep-IDS system consists of several interconnected modules, each

performing a specific function. These modules ensure that the system processes
network traffic efficiently while maintaining high detection accuracy. The Feature
Extraction Module identifies relevant network characteristics, such as packet size,

35
connection duration, and protocol usage, to distinguish between benign and
malicious activities. These extracted features are fed into the Deep Learning
Model, which leverages neural networks to analyze patterns and detect potential
threats. The model is trained on diverse datasets to improve accuracy and
adaptability to evolving cyber threats.

3.4.1 Data Collection Module

The data collection module plays a critical role in the development of an

effective intrusion detection system by gathering network traffic data from
multiple sources. This module is designed to ensure that the dataset used for
training and testing the deep learning model is diverse, comprehensive, and
representative of real-world network traffic, including both normal and malicious
activities. Without high-quality data, the performance of the CNN-based intrusion
detection system may be compromised, leading to lower accuracy and an
increased number of false positives or false negatives.

The data collection process involves acquiring network traffic data from two
primary sources. The first source is real-time network traffic generated by IoT
devices operating in a test environment. This data is captured using packet
sniffing tools such as Wireshark or tcpdump, which monitor and log all incoming
and outgoing packets on a network. By capturing real-time data, the system can
learn the natural behavior of IoT network communication, enabling it to
distinguish between legitimate and suspicious activities.

The second source of data includes publicly available intrusion detection datasets
that have been widely used in cybersecurity research. One of the most commonly
used datasets is NSL-KDD, which is an improved version of the original KDD99
dataset. This dataset contains labeled network traffic records classified into
different attack categories such as denial-of-service (DoS) attacks, remote-to-

36
local (R2L) attacks, user-to-root (U2R) attacks, and probing attacks. NSL-KDD
is specifically designed to address some of the limitations of its predecessor by
removing redundant records and balancing the distribution of attack and normal
traffic samples, making it a suitable choice for training deep learning models.

In addition to NSL-KDD, other datasets such as CICIDS2017 and UNSW-NB15

may also be used to enhance the diversity of the training data. The CICIDS2017
dataset was generated to simulate real-world network traffic by capturing normal
user activities and various types of cyberattacks, including brute-force attacks,
botnet activities, and port scanning attempts.

The UNSW-NB15 dataset provides a mixture of synthetic and real network

traffic, offering a more realistic representation of network behavior with various
types of attacks. By incorporating multiple datasets, the model can be trained to
recognize a broader range of attack patterns, improving its generalization
capabilities when deployed in real-world scenarios.

The collected network traffic data includes various features that help in
distinguishing between normal and malicious activities. Some of the key features
extracted from network packets include source and destination IP addresses,
protocol types, packet sizes, time intervals between packets, number of
connections within a given time frame, and flag status of TCP connections. These
features provide valuable insights into network behavior, enabling the model to
identify deviations from expected patterns that may indicate the presence of an
intrusion.

To ensure data integrity and consistency, the collected raw data undergoes initial
preprocessing, such as duplicate record removal and basic filtering to eliminate
noise. Any missing values in the dataset are handled appropriately to prevent
inconsistencies in the training process. Once the data collection phase is

37
complete, the processed dataset is stored in a structured format for further
preprocessing, feature selection, and model training.

The efficiency of the data collection module directly impacts the overall
performance of the intrusion detection system. A well-curated dataset that covers
a diverse range of attack scenarios and normal activities enhances the robustness
of the deep learning model, allowing it to adapt to new and emerging cyber
threats. By combining real-time IoT network traffic with standardized benchmark
datasets, Deep-IDS ensures that the intrusion detection system is capable of
identifying attacks with high accuracy while minimizing false alarms.

3.4.2 Data Preprocessing Module

The data preprocessing module is a crucial component of the system, ensuring

that the collected network traffic data is properly structured, optimized, and ready
for training the deep learning model. Raw network traffic data is often
inconsistent, noisy, and contains missing or redundant information, which can
negatively impact the performance of the convolutional neural network (CNN).
Effective preprocessing enhances the model’s ability to learn meaningful
patterns, reduces computational complexity, and improves classification
accuracy.

The preprocessing module systematically transforms raw network traffic data into
a structured format suitable for training. It consists of several important steps,
including data cleaning, normalization, feature encoding, and handling missing
values. Each of these steps plays a vital role in improving data quality and
ensuring efficient training of the deep learning model.

38
Data Cleaning

Raw network traffic logs collected from IoT devices or publicly available datasets
often contain duplicate entries, irrelevant records, and corrupted data points. Data
cleaning is performed to remove inconsistencies and refine the dataset for better
analysis. The cleaning process involves:

 Eliminating duplicate records: Redundant network packets may be

logged multiple times due to packet retransmission or network congestion.
Removing duplicates prevents the model from learning biased patterns and
ensures more accurate training.

 Filtering out irrelevant data: Some network traffic records may not
contribute to meaningful intrusion detection, such as unrelated background
traffic or incomplete packet captures. These records are discarded to focus
only on significant data points.

 Standardizing time formats and field values: Since network logs are
generated with timestamps and varying field formats, standardization
ensures uniformity across all records, making it easier for the deep learning
model to process the data.

By performing data cleaning, the system ensures that only high-quality, relevant
data is used for intrusion detection, preventing misleading patterns and improving
model efficiency.

Normalization

Normalization is an essential step in data preprocessing that involves scaling

numerical features to a standard range, ensuring that all attributes contribute
equally to the learning process. Network traffic data consists of numerical
attributes with varying scales, such as packet sizes, connection durations, and

39
byte counts. Since machine learning and deep learning models rely on numerical
inputs, inconsistent feature scales can negatively impact model performance.

Without normalization, features with large numerical values may dominate the
learning process, leading to biased predictions and slower convergence during
training. For example, if packet sizes range in thousands while connection
durations are measured in milliseconds, the model might prioritize packet size
over duration, skewing detection accuracy.

Several normalization techniques can be applied to IDS data preprocessing. Min-

max scaling transforms features to a fixed range, typically [0,1], preserving the
relationships between values. Z-score normalization standardizes data by
centering it around zero with unit variance, making it robust to outliers. Log
transformation can also be used to handle skewed distributions by compressing
large values.

By normalizing network traffic data, IDS models achieve better feature balance,
faster training convergence, and improved detection accuracy. Proper
normalization ensures that deep learning models effectively differentiate between
normal and malicious activities, enhancing the overall performance of intrusion
detection systems.

To ensure balanced feature representation, normalization techniques such as Min-

Max Scaling and Z-score Normalization are applied.

 Min-Max Scaling: Transforms numerical values into a fixed range,

typically between 0 and 1, using the formula:

40
This method ensures that all values lie within a specific range, making it easier
for the CNN model to process the data uniformly.

 Z-score Normalization: Converts numerical values into a standard normal

distribution with a mean of 0 and a standard deviation of 1, using the
formula:

where μ\mu is the mean and σ\sigma is the standard deviation.

Normalization helps in reducing bias caused by differing feature scales,

improving convergence speed during model training, and enhancing overall
classification performance.

Feature Encoding

Many network traffic datasets contain categorical attributes such as protocol type
(TCP, UDP, ICMP), service type (HTTP, FTP, SSH, etc.), and flag status
(SYN, ACK, FIN, etc.). Since deep learning models require numerical inputs,
categorical data must be transformed into a suitable numerical format through
feature encoding.

 One-Hot Encoding: Converts categorical variables into binary vectors,

where each unique category is represented as a separate column with 0s
and 1s. For example, if the protocol type has three categories (TCP, UDP,
ICMP), it is encoded as:

o TCP → [1, 0, 0]

o UDP → [0, 1, 0]

41
 o ICMP → [0, 0, 1]

 Label Encoding: Assigns a unique integer value to each category instead

of binary vectors. For instance, protocol types could be encoded as:

o TCP → 1

o UDP → 2

o ICMP → 3

One-hot encoding is preferred when categorical values are nominal (i.e., have no
intrinsic ordering), while label encoding is used for ordinal categories. The choice
of encoding method impacts how the CNN model interprets categorical features
and extracts meaningful relationships.

Handling Missing Values

Network traffic data may have missing or incomplete records due to network
disruptions, packet loss, or errors in data collection. If missing values are not
addressed, the deep learning model may struggle to learn meaningful patterns,
leading to incorrect predictions. The preprocessing module employs different
strategies for handling missing data:

 Imputation with Mean or Median: If a numerical attribute has missing

values, the mean or median of the existing values is used to fill the gaps.
This approach ensures that missing data does not introduce significant
biases.

 Mode Imputation for Categorical Features: The most frequent category

in a feature column is used to replace missing categorical values, ensuring
consistency in categorical data representation.

42
  Removing Highly Incomplete Records: If a record contains excessive
missing values that cannot be reasonably imputed, it is removed to
maintain dataset integrity.

Handling missing values ensures that the dataset remains complete and prevents
errors during model training, improving overall performance.

Impact of Data Preprocessing on CNN Model Performance

The preprocessing module plays a fundamental role in ensuring that the CNN-
based intrusion detection model receives high-quality, structured input data.
Without proper preprocessing, raw network traffic data may contain noise,
inconsistencies, and biases that could negatively affect model accuracy. The
impact of preprocessing on model performance includes:

 Improved Training Efficiency: Clean and normalized data reduces the

time required for model convergence, leading to faster training.

 Better Generalization: Properly scaled and encoded data allows the CNN
model to learn robust patterns, improving generalization to unseen network
traffic.

 Data Imbalance Handling – Cyberattack datasets often have an uneven

distribution of normal and malicious traffic. Techniques such as
oversampling, under sampling, and synthetic data generation (e.g.,
SMOTE) help balance the dataset, improving detection accuracy for
minority classes.

 Reduced False Positives and False Negatives: Removing redundant and

misleading data enhances the ability of the model to distinguish between
normal and malicious network activity.

43
  Data Consistency and Standardization – Ensuring uniform data formats
and handling missing values prevents inconsistencies that could mislead
the model.

 Encoding Categorical Data – Certain network attributes, such as protocol

types or connection states, are categorical and must be encoded into
numerical values using techniques like one-hot encoding or label encoding.

 Noise Reduction – Network traffic data may contain irrelevant or

redundant information, such as incomplete packets or duplicate entries.
Removing such noise helps the model focus on meaningful patterns.

 Dimensionality Reduction – High-dimensional datasets can slow down

training and introduce overfitting. Techniques like Principal Component

Analysis (PCA) and feature selection help retain the most relevant attributes
while improving computational efficiency.

By incorporating a well-defined preprocessing pipeline, Deep-IDS ensures that

network traffic data is optimized for deep learning-based intrusion detection. The
next module, feature selection, further refines the dataset by selecting the most
relevant attributes that contribute to effective classification.

3.4.3 Feature Selection Module

Feature selection plays a vital role in reducing the dimensionality of the dataset
while retaining relevant information. This module extracts key features from
network traffic, such as:

 Protocol type

 Source and destination IP addresses

 Packet size

44
  Flow duration

 TCP flags

 Number of connections in a given timeframe

By selecting the most significant features, the system improves computational

efficiency and enhances the model’s ability to detect anomalies.

3.4.4 CNN-Based Deep Learning Model

The core of Deep-IDS is a convolutional neural network (CNN) that classifies

network traffic as either normal or malicious. CNNs are widely used for pattern
recognition tasks and have shown remarkable success in cybersecurity
applications.

The CNN architecture consists of multiple layers, including:

 Input Layer – Receives preprocessed network traffic data.

 Convolutional Layers – Extracts spatial features using filters and feature

maps.

 Pooling Layers – Reduces dimensionality and computational complexity.

 Fully Connected Layers – Combines extracted features for final

classification.

 Output Layer – Produces a binary classification output (normal or attack).

The CNN model is trained using labeled datasets, where attack patterns are
identified and classified into different categories such as denial-of-service (DoS),
probing, and remote-to-local (R2L) attacks.

45
 Figure 3.3 CNN Model Architecture for Intrusion Detection

3.4.5 Detection System

The detection system is a critical module in Deep-IDS that continuously monitors

network traffic and classifies it as either normal or malicious based on the trained
CNN model. After the CNN model processes network activity and identifies
potential threats, this module takes appropriate actions to ensure network security.
It functions as a real-time monitoring unit, analyzing incoming data packets,
extracting relevant features, and making predictions based on learned attack
patterns.

Once a potential intrusion is detected, the system generates alerts that notify
network administrators of suspicious activity. These alerts contain detailed
information, including the attack type, severity level, source and destination IP
addresses, and timestamps. The system can also integrate with security

46
mechanisms such as firewalls and intrusion prevention systems (IPS) to block
malicious traffic automatically.

The detection module ensures minimal false positives and false negatives by
leveraging the high accuracy of the CNN model. It continuously updates itself by
learning from new traffic patterns, making it adaptive to emerging cyber threats.
Furthermore, the module maintains a log of all detected intrusions, which can be
analyzed to improve future threat detection. This real-time detection and response
mechanism enhances the overall security of IoT networks, preventing
unauthorized access and mitigating cyberattacks efficiently.

3.4.6 Web-Based User Interface

To enhance usability, Deep-IDS includes a web-based interface that allows

administrators to monitor network security in real time. This intuitive dashboard
provides a comprehensive view of network activity, detected threats, and system
performance, enabling quick decision-making and response to potential
intrusions. The interface provides:

 A dashboard displaying network traffic statistics

 Graphical representations of detected intrusions

 Logs of attack attempts and their classifications

 Customizable alert settings for security administrators

The user-friendly web interface ensures that system administrators can efficiently
manage and respond to security threats.

47
Figure 3.4 Web-Based Interface for Deep-IDS

48
3.5 Workflow of Deep-IDS

The workflow of Deep-IDS follows a structured sequence of steps that ensure

efficient detection and classification of intrusions.

1. Network traffic data is collected from IoT devices and datasets.

2. Preprocessing steps clean, normalize, and extract relevant features.

3. Feature selection is applied to enhance detection efficiency.

4. The CNN model is trained using labeled datasets.

5. The trained model is deployed for real-time intrusion detection.

6. Network traffic is continuously monitored, and any detected threats are

classified.

7. The web interface provides real-time visualization and logs for further
analysis.

3.6 Summary

This chapter described the system design of Deep-IDS, including its architecture,
block diagram, module descriptions, and overall workflow. The use of CNN-
based deep learning improves the system's accuracy and adaptability to evolving
cyber threats.

By implementing real-time monitoring, feature selection, and an interactive web

interface, Deep-IDS provides a comprehensive intrusion detection solution for
IoT environments. The next chapter will cover the implementation details,
including model training, evaluation, and deployment.

49
 CHAPTER 4: IMPLEMENTATION

4.1 Dataset Used

The implementation of Deep-IDS begins with the careful selection and utilization
of datasets that accurately represent the diverse network traffic encountered in
IoT environments. The primary dataset employed in this project is the NSL-KDD
dataset, a refined version of the original KDD99 dataset, which addresses issues
such as redundant records and class imbalance. The NSL-KDD dataset provides
a well-structured set of labelled network traffic records, categorizing various
forms of intrusions, including denial-of-service (DoS) attacks, probing, remote-
to-local (R2L) attacks, and user-to-root (U2R) attacks.To enhance model
robustness, additional datasets such as CICIDS2017 and UNSW-NB15 may be
integrated. CICIDS2017 captures realistic network traffic with a wide range of
modern cyber threats, while UNSW-NB15 offers a more recent and
comprehensive dataset with diverse attack types and realistic background traffic.
The inclusion of multiple datasets ensures that the deep learning model is exposed
to varied attack patterns and normal behaviours, improving its ability to
generalize and detect novel intrusions effectively.Beyond dataset selection,
preprocessing plays a vital role in preparing the data for deep learning. Key
preprocessing steps include data cleaning, feature selection, normalization, and
encoding categorical attributes. Normalization ensures that numerical features
like packet size and connection duration are scaled properly, preventing large
values from dominating the learning process. One-hot encoding or label encoding
is applied to categorical variables such as protocol types and service names to
make them compatible with neural networks.By leveraging multiple datasets and
implementing comprehensive preprocessing techniques, Deep-IDS enhances its
ability to detect sophisticated cyber threats in IoT environments. This approach
ensures that the model is well-equipped to handle real-world scenarios, providing
a reliable and scalable intrusion detection solution.

50
4.2 Data Preprocessing Techniques

Data preprocessing is a critical phase in the implementation of Deep-IDS, as it

transforms raw network traffic data into a structured format suitable for deep
learning. The preprocessing pipeline consists of several key steps to ensure data
quality and consistency.

First, data cleaning removes duplicate records, irrelevant entries, and corrupted
data points. Next, normalization scales numerical features, preventing larger
values from dominating the learning process and improving CNN model
performance. Categorical variables, such as protocol types and service names, are
converted into numerical values using one-hot encoding or label encoding,
making them compatible with the model.

Handling missing values is another essential step. Missing data is either imputed
using statistical methods (mean, median, or mode) or removed if excessive gaps
exist. These preprocessing techniques collectively enhance data reliability,
ensuring the model receives optimized and noise-free input for accurate intrusion
detection.

4.3 Deep Learning Model Development

The core of Deep-IDS is the CNN-based deep learning model developed to

classify network traffic into normal and malicious categories. The model
architecture is designed to automatically extract hierarchical features from the
preprocessed data. The development process begins with defining the input layer
that receives the structured data, followed by multiple convolutional layers that
apply filters to detect spatial features in the network traffic. Pooling layers are
interspersed to reduce the dimensionality of the feature maps, thereby lowering
computational complexity and enhancing the model’s robustness. Fully
connected layers integrate the learned features to form a comprehensive

51
representation before the final classification is made through an output layer that
typically uses a sigmoid activation function for binary classification. The model
is trained using the labeled dataset, where a portion of the data is reserved for
validation to monitor the performance and avoid overfitting. Optimization
techniques such as Adam optimizer are employed alongside appropriate loss
functions like binary cross-entropy to ensure that the model converges to an
optimal solution. Hyperparameter tuning is conducted iteratively to refine the
model’s accuracy and reduce false positive rates.

4.4 System Deployment

After the successful training and evaluation of the CNN model, the next step
involves deploying Deep-IDS in a real-time environment. The system is
implemented as a Flask web application, which provides a user-friendly interface
for monitoring network traffic and intrusion alerts. The deployment architecture
is designed to integrate the trained model into a continuous monitoring system
that analyzes live network traffic from IoT devices. Real-time data is captured,
preprocessed, and fed into the CNN model, allowing for immediate classification
of network activity.

When an anomaly or potential intrusion is detected, the system triggers alerts and
logs the incident for further analysis. The deployment phase also involves
optimizing the model for resource-constrained environments, often by leveraging
techniques such as model quantization or converting the model into a lightweight
format suitable for edge devices. This approach ensures that Deep-IDS not only
maintains high accuracy but also operates efficiently in real-time, providing
robust security monitoring for IoT networks.

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4.5 Summary

This chapter detailed the implementation aspects of Deep-IDS, covering dataset

selection, data preprocessing, model development, and deployment strategies for
real-time intrusion detection.

The use of diverse datasets ensures that the system is trained on a wide range of
attack scenarios, improving its ability to detect evolving cyber threats.

Comprehensive data preprocessing techniques, including cleaning,

normalization, and feature encoding, enhance the quality of input data, ensuring
accurate and efficient learning. The CNN-based deep learning model plays a
crucial role in classifying network traffic, leveraging convolutional layers for
feature extraction and fully connected layers for decision-making.

Optimization techniques such as hyperparameter tuning, dropout regularization,

and the Adam optimizer further refine the model’s accuracy and reduce false
positives.

The final deployment strategy integrates the trained model into a real-time
monitoring system, providing administrators with an interactive web-based
interface for threat detection and response.

The combination of these components results in a robust, scalable, and intelligent

intrusion detection system, capable of adapting to the dynamic security
challenges in IoT environments.

The next chapter will evaluate the system’s performance, analyzing its accuracy,
efficiency, and effectiveness in detecting network intrusions.

53
 CHAPTER 5: SOURCE CODE

5.1Code For Model Building and Predictions

Importing the Necessary Libraries and Packages

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense,
Dropout
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.metrics import accuracy_score, classification_report,
confusion_matrix
from imblearn.over_sampling import SMOTE
from collections import Counter

# Load dataset
train = pd.read_csv('kdd_train.csv')

# Convert categorical features into numerical values

train['protocol_type'] = train['protocol_type'].map({'tcp': 1, 'udp': 2, 'icmp': 3})

54
train['flag'] = train['flag'].map({
'SF': 1, 'S0': 2, 'REJ': 3, 'RSTR': 4, 'SH': 5,
'RSTO': 6, 'S1': 7, 'RSTOS0': 8, 'S3': 9, 'S2': 10, 'OTH': 11
})
train['flag'] = train['flag'].astype(int)
train['protocol_type'] = train['protocol_type'].astype(int)

# Label encoding for attack categories

attack_labels = [
'teardrop', 'pod', 'back', 'land', 'warezclient', 'portsweep',
'guess_passwd', 'ftp_write', 'multihop', 'rootkit', 'buffer_overflow',
'imap', 'loadmodule', 'phf', 'spy', 'perl', 'warezmaster'
]
train['labels'] = train['labels'].apply(lambda x: 'attacker' if x in attack_labels else
x)

# Selecting Features & Target

X = train[['protocol_type', 'flag', 'src_bytes', 'dst_bytes', 'hot', 'count',
'srv_count', 'same_srv_rate', 'dst_host_count', 'dst_host_srv_count',
'dst_host_same_srv_rate', 'dst_host_diff_srv_rate',
'dst_host_same_src_port_rate', 'dst_host_rerror_rate']]
y = train['labels']

# Encode target variable

label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)

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# Apply SMOTE to balance the dataset
smote = SMOTE(k_neighbors=2)
X, y = smote.fit_resample(X, y)
print('Classes and number of values after SMOTE:', Counter(y))

# Normalize features
scaler = StandardScaler()
X = scaler.fit_transform(X)

# Reshape X for CNN input (Convert 2D to 3D)

X = np.expand_dims(X, axis=2) # CNN expects 3D input (samples, timesteps,
features)

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
random_state=9)

# Build CNN Model

model = Sequential([
Conv1D(filters=64, kernel_size=3, activation='relu',
input_shape=(X_train.shape[1], 1)),
MaxPooling1D(pool_size=2),
Conv1D(filters=128, kernel_size=3, activation='relu'),
MaxPooling1D(pool_size=2),
Flatten(),
Dense(128, activation='relu'),
Dropout(0.5),

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 Dense(64, activation='relu'),
Dense(len(np.unique(y)), activation='softmax') # Output layer with softmax
for classification
])

# Compile model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

# Train the model

history = model.fit(X_train, y_train, epochs=20, batch_size=32,
validation_data=(X_test, y_test))

# Evaluate Model
y_pred = np.argmax(model.predict(X_test), axis=1)
accuracy = accuracy_score(y_test, y_pred)
print("CNN Model Accuracy:", accuracy)
print("Classification Report:\n", classification_report(y_test, y_pred))

# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(6, 5))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix")

57
plt.show()

# Save the model

model.save('intrusion_cnn_model.h5')

# Load the model

loaded_model = keras.models.load_model('intrusion_cnn_model.h5')

CHAPTER 6: EXPERIMENTAL RESULTS AND ANALYSIS

6.1 Introduction

This chapter presents the evaluation results of the Deep-IDS model, focusing on
its accuracy, performance metrics, and comparison with traditional intrusion
detection systems (IDS). The model's effectiveness is assessed using key
performance indicators, including accuracy, precision, recall, F1-score, and a
confusion matrix, which help determine its ability to correctly classify normal
and malicious network traffic.

To highlight its advantages, Deep-IDS is compared with traditional IDS methods

such as signature-based and anomaly-based detection systems. The evaluation
explores how deep learning enhances detection accuracy and adaptability to
evolving cyber threats, addressing limitations in conventional approaches.

Additionally, this chapter includes visual representations, such as screenshots and

figures from the web application, demonstrating Deep-IDS’s user interface and
real-time monitoring capabilities. These visuals provide insights into how
administrators can interact with the system, monitor threats, and take necessary

58
security actions. The results and analysis presented here validate the effectiveness
of Deep-IDS in securing IoT environments against cyber threats.

6.2 Model Evaluation and Accuracy

The evaluation of Deep-IDS is primarily based on its ability to classify network

traffic correctly. The model's accuracy is computed by comparing its predictions
to the ground truth labels in the test set. Accuracy is defined as the percentage of
correctly classified instances (both normal and malicious) out of the total number
of instances.

Accuracy Calculation:

The accuracy of the model can be calculated using the formula:

Where:

 True Positives (TP): Instances correctly classified as attacks.

 True Negatives (TN): Instances correctly classified as normal traffic.

 False Positives (FP): Instances incorrectly classified as attacks.

 False Negatives (FN): Instances incorrectly classified as normal traffic.

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Based on the evaluation results from the test dataset, Deep-IDS achieved an
impressive accuracy of approximately 97%, demonstrating its exceptional
performance in detecting intrusions while keeping false positives at a minimum.

This high accuracy can be attributed to several key factors:

 The robustness of the CNN model, which effectively extracts complex

patterns from network traffic.

 The use of a diverse and well-balanced dataset, enabling the model to

generalize well to unseen attack patterns.

 Advanced preprocessing techniques, such as feature normalization and

encoding, which helped the model learn more effectively.

 Hyperparameter tuning and optimization strategies, ensuring the model

achieves optimal performance without overfitting.

These results validate Deep-IDS as a highly reliable intrusion detection system,

capable of providing strong cybersecurity defense for IoT environments against
both known and evolving cyber threats.

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 Figure 6.1: Web Application Home Page

The home page displays key metrics such as the total number of network requests,
the number of intrusions detected, and system health indicators. It serves as a
dashboard for the system administrator to quickly assess the state of the intrusion
detection system.

6.3 Performance Metrics

Beyond accuracy, other performance metrics such as precision, recall, F1-score,

and confusion matrix provide a deeper understanding of how well the model
distinguishes between normal and malicious traffic.

Precision and Recall:

 Precision measures the proportion of correctly predicted attacks (true

positives) out of all predicted attacks (true positives and false positives).
High precision indicates that the model generates fewer false alarms.

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  Recall measures the proportion of correctly predicted attacks (true
positives) out of all actual attacks (true positives and false negatives). High
recall indicates that the model correctly identifies most attacks.

Deep-IDS achieved a precision of 95% and recall of 98%, suggesting that the
system is highly effective in both minimizing false alarms and detecting most
attacks.

F1-Score:

The F1-score is the harmonic mean of precision and recall and provides a
balanced measure of the model’s ability to classify both classes correctly.

For Deep-IDS, the F1-score was 96.5%, reflecting the model’s strong
performance in balancing the need for accurate detection without overwhelming
the system with false positives. This high F1-score indicates that the model
effectively minimizes both false positives and false negatives, ensuring reliable
threat detection. The precision-recall trade-off is optimized, allowing the system
to detect even sophisticated attack patterns while maintaining a low false alarm
rate. Additionally, the model's ability to generalize across different network

62
environments makes it suitable for real-world deployment in IoT security
frameworks. Continuous refinements, such as adaptive learning techniques and
enhanced feature selection, can further improve its performance and resilience
against evolving cyber threats.

Confusion Matrix:

The confusion matrix is another critical tool used to evaluate the performance of
classification models. It provides a visual representation of the model’s
predictions compared to the actual labels, detailing the true positives, false
positives, true negatives, and false negatives.

Figure 6.2: Confusion Matrix

The confusion matrix helps determine if the model is biased toward predicting
one class over the other. A high false positive rate means normal traffic is

63
incorrectly flagged as malicious, leading to unnecessary alerts. Similarly, a high
false negative rate indicates missed attacks, which can compromise security.
Analyzing this matrix allows for fine-tuning the model to improve accuracy and
balance detection.

6.4 Comparison with Traditional IDS

To further validate the effectiveness of Deep-IDS, a comparison is made with

traditional intrusion detection systems, including signature-based and anomaly-
based methods.

Signature-Based IDS:

Signature-based IDS detects threats by matching network traffic against a

database of known attack signatures. While this method is highly effective for
recognizing previously identified threats, it fails to detect new or unknown attack
patterns, making it ineffective against zero-day attacks.

In our evaluation, the signature-based IDS achieved an accuracy of approximately

85%, which is significantly lower than Deep-IDS. This limitation arises because
signature-based systems rely on frequent database updates, and they cannot detect
attacks that do not match pre-existing patterns. Additionally, they may struggle
against polymorphic malware and advanced persistent threats (APTs) that
continuously evolve to bypass signature detection.

On the other hand, Deep-IDS, powered by deep learning and CNN-based

architecture, demonstrated superior accuracy and adaptability. Unlike signature-
based IDS, Deep-IDS learns from network behavior and generalizes attack
patterns, allowing it to identify both known and unknown threats effectively. This
makes Deep-IDS a more scalable and future-proof solution for securing modern
IoT environments against evolving cyber threats.

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Anomaly-Based IDS:

Anomaly-based IDS identifies deviations from normal network behavior, making

it effective in detecting previously unknown attacks. However, a major
drawback of this approach is its high false positive rate, as legitimate deviations
are often misclassified as potential threats.

Our evaluation showed that an anomaly-based IDS achieved a precision

of only 80%, indicating a significant number of false alerts. Additionally, its
recall was lower, meaning it failed to detect certain types of attacks effectively.

In contrast, Deep-IDS, leveraging its CNN-based architecture,

demonstrated superior performance across key metrics, including accuracy,
precision, recall, and F1-score. The ability of deep learning to automatically
extract and learn complex patterns from network traffic contributed to its
improved detection rates.

Deep-IDS effectively minimized false positives while maintaining high

recall, ensuring that even zero-day attacks were identified with greater accuracy.
This comparison highlights the advantage of deep learning-based IDS over
traditional methods, proving its efficiency, adaptability, and robustness in
securing IoT environments against evolving cyber threats

65
6.5 System Performance and Interface Screenshots

Model Building Page:

Figure 6.3: Model Building Page

This page provides real-time visualizations of the model’s training and validation
accuracy, allowing users to monitor how the model improves as it learns from the
dataset. The interactive graphs help in assessing the convergence and
performance trends over different training epochs.

Additionally, the model-building page includes an interface for configuring

hyperparameter settings, such as the number of layers, learning rate, batch size,
and activation functions. Users can experiment with different configurations and
retrain the model to optimize its detection accuracy and efficiency.

This feature enables continuous model refinement, ensuring that Deep-IDS

remains adaptable to evolving cyber threats and provides highly accurate
intrusion detection tailored to specific network environments.

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Prediction Page:

Figure 6.4: Prediction Page

This page allows the administrator to see the live predictions made by the CNN
model. Each network packet is classified, and the system provides real-time alerts
when an attack is detected. This page also shows the prediction confidence and
provides insights into the specific type of attack identified.

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Performance Analysis Page:

Figure 6.5: Performance Analysis Page

This page gives a detailed breakdown of the model’s performance during testing.
It includes graphs, confusion matrices, and detailed numerical metrics, allowing
administrators to evaluate the system’s efficiency and accuracy.

Numerical Metrics: Precise values for accuracy, precision, recall, and F1-score,
allowing administrators to assess the model’s effectiveness in detecting various
types of cyber threats.

By analyzing this data, administrators can fine-tune the model, optimize detection
thresholds, and ensure that Deep-IDS maintains high reliability in real-world
cybersecurity applications
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Performance Evaluation
This section presents the performance evaluation of the Deep- IDS developed in
this paper. It starts with the evaluation matrices. Then, the confusion matrix was
analyzed to evaluate classification performance. After that, the intrusion detection
rate and the IDS’s response time have been assessed.

Evaluation Metrics
The state-of-the-art machine learning evaluation metrics have been used in this
paper to evaluate the performance of the proposed Deep-IDS. These evaluation
metrics are calculated using True Positive (TP), True Negative (TN), False
Positive (FP), and False Negative (FN) values from the confusion matrix
illustrated in figure. The accuracy, precision, recall (sensitivity), and F1 Score are
defined by equations and respectively. Another evaluation metric is FAR, which
is the False Positive Rate (FPR).

Confusion Matrix

the confusion matrix illustrated in figure 7 demonstrates the performance of the

proposed Deep-IDS to classify network traffic into one of the six classes. Upon
evaluating the system’s performance using Accuracy, Recall, Precision, and F1-
Score, it is evident that it performs well in identifying these categories. The
system achieves an overall accuracy of 97.67%. The recall values for BRF and
Benign instances are 0.970, while DDoS, DoS, MITM, and RP attacks have a
higher recall of 0.980.
The precision values indicate the system’s effectiveness in identifying true
positives. BRF and MITM attacks have a precision of 0.990, while Benign
instances have the highest precision of 1.00. DDoS attacks have a precision of
0.990, and DoS and RP attacks have a slightly lower precision of 0.960. The F1-

69
Scores, representing the harmonic mean of recall and precision, are 0.980 for
BRF, 0.985 for Benign and DDoS, 0.970 for DoS and RP, and 0.985 for MITM
attacks. In summary, the Deep-IDS exhibits high accuracy, recall, precision, and
F1 scores, indicating its strong performance in classifying different types of
intrusions. The overall performance for each class of the Deep-IDS is listed in
table

The exceptional results obtained from the confusion matrix analysis of

Deep-IDS underscore its effectiveness in the real- time identification and
classification of network intrusions.
The high recall and precision values across all categories, particularly for
critical intrusion vectors such as DDoS, MITM, and BRF attacks, affirm the
system’s robustness and reliability in distinguishing between benign and
malicious traffic with minimal error. The system’s ability to achieve a perfect
precision score of 1.00 for benign instances highlights its capability to accurately
identify legitimate network activities, thus reducing the likelihood of false
positives that could disrupt normal network operations. Moreover, the high F1
scores indicate a balanced performance between recall and precision, ensuring that
the system is not only accurate but also consistent in its intrusion detection
capabilities. This bal- ance is crucial for maintaining network security and integrity,
especially in dynamic and complex IoT environments where the cost of false
negatives or positives can be substantial. These metrics provide compelling
evidence of its potential to significantly enhance cybersecurity defenses against
an evolving landscape of cyber threats.

Performance Comparison
The Table presents the performance comparison between the proposed Deep-IDS
and other similar approaches. The proposed Deep-IDS demonstrates superior
performance in intrusion detection compared to several existing approaches, as
70
evidenced by a comprehensive performance compar- ison. Deep-IDS
outperforms other notable systems with an accuracy of 97.67%, precision of
97.67%, a recall of 98.17%, and an F1-Score of 97.91%. For instance, Ashiku
et al. reported an accuracy of 94.40% but did not provide figures for precision,
recall, or F1-Score, indicating a narrower focus on accuracy alone. Similarly,
Musleh et al. achieved a commendable balance with 92.40% accuracy, 89.10%
precision, and a 92% F1-Score, yet fell short of the comprehensive performance
metrics offered by Deep-IDS. Notably, Logeswari et al. , and Mebawondu et al.
presented systems with significantly lower overall performance metrics,
highlighting the advanced capabilities of Deep-IDS in handling various intrusion
types effectively. Elsayed et al. and Kumar et al. also proposed systems with high
accuracy rates of 96.56% and 97.45%, respectively, but neither matched the
balanced performance across all metrics achieved by Deep-IDS. This comparison
underscores the robustness and efficiency of Deep-IDS in accurately detecting
and classifying intrusions, setting a new benchmark in the field of cybersecurity
for IoT systems.

6.6 Summary

This chapter provided a comprehensive evaluation of Deep-IDS, highlighting its

superior performance compared to traditional intrusion detection systems. The
model demonstrated high accuracy, precision, recall, and F1-score, proving its
effectiveness in identifying a wide range of network intrusions, including zero-
day attacks.

Deep-IDS was tested against signature-based and anomaly-based IDS

methodologies, where it consistently outperformed both in terms of detection
accuracy and adaptability. While signature-based IDS struggled with new attack
patterns, and anomaly-based IDS had a higher false positive rate, Deep-IDS

71
achieved a balanced performance with lower false alarms and better
generalization.

Additionally, the web application interface was reviewed, showcasing its real-
time monitoring capabilities, detailed performance analysis, and interactive user
controls. Screenshots were presented to illustrate how administrators can use the
system for live network analysis and security insights.

Overall, the results confirm that Deep-IDS is a robust, scalable, and future-proof
solution for securing IoT networks against both known and emerging cyber
threats. The next chapter will explore potential improvements, deployment
considerations, and future research directions to further enhance the system’s
effectiveness.

CHAPTER 7: CONCLUSION AND FUTURE WORK

7.1 Conclusion

The Deep-IDS project presents a robust, deep learning-based solution for

real-time intrusion detection in IoT networks. By leveraging Convolutional
Neural Networks (CNNs), the system effectively classifies network traffic as
either normal or malicious, offering significant improvements in accuracy and
efficiency compared to traditional intrusion detection systems. The use of
publicly available datasets such as NSL-KDD, combined with advanced
preprocessing techniques, ensures that the model is well-equipped to detect a
wide variety of attack patterns, including both known and unknown threats. The
system achieves impressive performance metrics, including high accuracy,
precision, recall, and F1-score, while maintaining low false positive rates. These
results demonstrate the potential of deep learning techniques to address the
complex security challenges faced by IoT networks, which are often vulnerable
to a wide range of cyberattacks due to their inherent resource constraints.

72
The implementation of Deep-IDS as a web-based application further enhances its
usability, allowing system administrators to monitor real-time traffic, review
performance metrics, and receive alerts when potential intrusions are detected.
The user-friendly interface provides administrators with quick insights into
network security and enables them to take timely action to mitigate risks.
Compared to traditional signature-based and anomaly-based IDS, Deep-IDS
offers superior adaptability, scalability, and accuracy, making it a valuable tool
for securing IoT environments. The system not only enhances the security of IoT
devices but also offers a promising approach for the future development of
intelligent, adaptive IDS solutions in rapidly evolving cyber threat landscapes.

7.2 Future Work

While Deep-IDS has demonstrated considerable success in its current

form, there are several areas for future improvement and expansion. One
significant enhancement could be the integration of federated learning, which
would enable the model to learn from distributed IoT networks without the need
to centralize sensitive data. This approach could help mitigate privacy concerns
while improving the model's ability to generalize across different IoT
environments. Additionally, incorporating unsupervised learning techniques,
such as autoencoders or generative adversarial networks (GANs), could allow the
system to detect previously unseen or emerging attack patterns without requiring
large amounts of labeled data. This would further strengthen Deep-IDS’s ability
to handle zero-day attacks and new vulnerabilities that traditional IDS systems
might miss.

Another area for future work is improving the system’s real-time performance on
resource-constrained IoT devices. Currently, deep learning models, particularly
CNNs, can be computationally intensive. Optimizing the model for deployment

73
on edge devices, perhaps through model pruning, quantization, or knowledge
distillation, would allow Deep-IDS to operate efficiently even with limited
hardware resources. Furthermore, the integration of blockchain technology could
offer an immutable and transparent logging mechanism for intrusion events,
enhancing system security and traceability. Future work could also explore the
integration of Deep-IDS with other security mechanisms, such as intrusion
prevention systems (IPS) and automated response frameworks, to create a more
comprehensive security solution. These advancements would not only improve
the overall performance of Deep-IDS but also broaden its applicability to a wider
range of IoT use cases.

The proposed Deep-IDS uses a Raspberry Pi 4 Model B with 4GB primary

memory. It is a headless computer and an expensive device [73]. It has been
designed to perform many other sophisticated computations. That is why it is
an expensive device. In this experiment, it was used only for intrusion detection.
An embedded system exclusively designed for the Deep-IDS would reduce the
implementation cost. However, developing an embedded system is beyond this
research’s scope. It creates a new opportunity to conduct further research to make
IDS hardware cost-effective.

Adversarial Machine Learning (Aml) Attack

The proposed Deep-IDS demonstrates outstanding perfor- mance as an intrusion
detector. However, no counter- measurement has been taken for the AML
attack. The CIC-IDS2017 is a public dataset. Anyone can access and analyze the
data to prepare sequences for an AML attack [74]. Even though AML attacks have
been drawing significant attention lately, they are not within the proposed Deep-
IDS context. However, it opens the door to conducting more experiments to
defend against AML attacks and secure the proposed Deep-IDS.

74
Real-World Experiment
The proposed Deep-IDS has been experimented with in a testbed that resembles
a real-world scenario. As a result, the performance of the proposed system is
considered a realistic result. However, a testbed does not encompass a large
perimeter like a realistic environment. It is a significant limitation of the proposed
system.

Cyber-Physical System Security

This paper focuses on intrusion detection only. However, a system is always
vulnerable to cyber-physical intrusion unless necessary counter-measurement is
taken. Anyone with access to the edge server has the scope to make the entire
system vulnerable by gaining unauthorized access or altering the IDS’s
parameters.
The proposed Deep-IDS’s existing limitations are the future scopes of further
research. Eventually, more weak- nesses of this system will be discovered, and
in subsequent research, those weaknesses will be strengthened. This is how,
through continuous improvement, the proposed Deep-IDS will be an efficient,
effective, and unique IDS.

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