ACE ENGINEERING COLLEGE

A MACHINE LEARNING BASED CYBER ATTACK

DETECTION MODEL FOR WIRELESS SENSOR
NETWORKS IN MICROGRIDS
Major Project Stage-I Report

Submitted in partial fulfillment of the requirements for the award of the Degree of

Bachelor of Technology (B. Tech)

in

COMPUTER SCIENCE AND ENGINEERING

by

MENDE PUJA 22AG5A0501

B ANJALI 21AG1A0509
LAVUDYA HARSHITHA 21AG1A0532
KARRE BHARADWAJ 21AG1A0526

Under the Esteemed Guidance of

Dr. M. V. Vijaya Saradhi
Professor & Dean-CSE

Department of Computer Science and Engineering

ACE ENGINEERING COLLEGE
An AUTONOMOUS Institution
NBA Accredited B. Tech Courses, Accorded NAAC ‘A’ Grade
(Affiliated to Jawaharlal Nehru Technological University, Hyderabad, Telangana)
Ankushapur (V), Ghatkesar (M), Medchal – Malkajgiri Dist - 501 301.
FEB - 2025

Department of Computer Science and Engineering

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ACE
Engineering College
An AUTONOMOUS Institution
NBA Accredited B. Tech Courses, Accorded NAAC ‘A’ Grade
Website: www.aceec.ac.in E-mail: info@aceec.ac.in

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

CERTIFICATE
This is to certify that the Major project Stage-I work entitled “A MACHINE
LEARNING BASED CYBER ATTACK DETECTION MODEL FOR WIRELESS
SENSOR NETWORKS IN MICROGRIDS” is being submitted by Mende Puja
(22AG5A0501), B Anjali (21AG1A0509), Lavudya Harshitha (21AG5A0532), Karre
Bharadwaj (22AG1A0526) in partial fulfillment for the award of Degree of BACHELOR OF
TECHNOLOGY in COMPUTER SCIENCE AND ENGINEERING to the Jawaharlal Nehru
Technological University, Hyderabad during the academic year 2024-25 is a record of bonafide work
carried out by them under our guidance and supervision.

The results embodied in this report have not been submitted by the student to any other
University or Institution for the award of any degree or diploma.

Internal Guide HoS Dean-CSE

Dr.M.V.Vijaya Saradhi Dr.Ch.Vijaya Kumar Dr.M.V.Vijaya Saradhi
Professor & Dean-CSE Associate Professor Professor & Dean-CSE

Project Coordinator EXTERNAL EXAMINER

Dr. V. Ravi Kumar
Associate Professor & HoS

Department of Computer Science and Engineering

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ACKNOWLEDGEMENT
We would like to express our gratitude to all the people behind the screen who have
helped us transform an idea into a real-time application.
We would like to express our heartfelt gratitude to our parents without whom we would
not have been privileged to achieve and fulfill our dreams.
A special thanks to our General Secretary, Prof. Y. V. Gopala Krishna Murthy, for
having founded such an esteemed institution. Sincere thanks to our COO Mr. Y.V.Raghu
Vamshi, for support in doing project work. We are also grateful to our beloved principal, Dr.
B. L. RAJU for permitting us to carry out this project
We profoundly thank Dr. M. V. Vijaya Saradhi, Professor and Dean of the Department
of Computer Science and Engineering, who has been an excellent guide and also a great source
of inspiration for our work.
We extremely thank Dr Ch Vijaya Kumar, HoS & Associate Professor, who helped
us in all the way in fulfilling of all aspects in completion of our Major-Project Satge-I.
We extremely thank Dr. V. Ravi Kumar, Associate Professor and Project Coordinator,
who helped us all the way in fulfilling all aspects of the completion of our Major-Project Stage-I.
We are very thankful to our internal guide Dr.M.V.Vijaya Saradhi, Professor and
Dean of the Department of Computer Science and Engineering, who has been an excellent
and also given continuous support for the Completion of our project work.
The satisfaction and euphoria that accompany the successful completion of the task
would be great, but incomplete without the mention of the people who made it possible, whose
constant guidance and encouragement crown all the efforts with success. In this context, we
would like to thank all the other staff members, both teaching and non-teaching, who have
extended their timely help and eased our task.

BY
MENDE PUJA (22AG5A0501)
B ANJALI (21AG1A0509)
LAVUDYA HARSHITHA (21AG1A0532)
KARRE BHARADWAJ (21AG1A0526)
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INDEX
S. NO CONTENTS PAGE. NO
ABSTRACT 1
1. INTRODUCTION 4
2. LITERATURE SURVEY 7
3. SOFTWARE REQUIREMENT AND ANALYSIS 10
3. 1 PROBLEM STATEMENT 10
3. 2 MODULES AND THEIR FUNCTIONALITIES 10
4. SOFTWARE DESIGN 12
4. 1 ARCHITECTURE DIAGRAM 12
4. 2 DATAFLOW DIAGRAMS 14
4. 2. 1 DFD LEVEL - 0 14
4. 2. 2 DFD LEVEL - 1 15
4. 2. 3 DFD LEVEL - 2 16
4. 3 UML DIAGRAMS 18
4. 3. 1 USECASE DIAGRAM 18
4. 3. 2 CLASS DIAGRAM 19
4. 3. 3 SEQUENCE DIAGRAM 21
4. 3. 4 COMPONENT DIAGRAM 22
4. 3. 5 ACTIVITY DIAGRAM 23
4. 3. 6 DEPLOYMENT DIAGRAM 24
4.4 METHODOLOGY 25
4.4.1 CONVOLUTIONAL NEURAL NETWORKS 25
4.4.2 PASSIVE AGGRESSIVE CLASSIFIERS 25
4.4.3 RANDOM FOREST CLASSIFIERS 26
4.4.4 XBOOST CLASSIFIERS 27
5. SOFTWARE AND HARDWARE REQUIREMENTS 28
5. 1 SOFTWARE REQUIREMENTS 28
5. 2 HARDWARE REQUIREMENTS 28
6. CONCLUSIONS 29
7. REFERENCES 30
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ABSTRACT

In this project, we demonstrate the context of cyber threats, explores the security challenges in wireless
sensor networks (WSNs) within microgrids, emphasizing the need for robust cyber-attack detection
mechanisms. The study introduces a machine learning-driven approach tailored for identifying network
anomalies and mitigating potential threats. By leveraging a diverse set of advanced algorithms—
including Convolutional Neural Networks (CNN), Passive Aggressive Classifiers, Random Forest
Classifiers, and XGBoost Classifiers—the model efficiently classifies network states as normal or
under specific attack scenarios, such as grayhole, blackhole, or flooding attacks.

Utilizing a comprehensive dataset from Kaggle, the system is trained to recognize intricate patterns
associated with cyber intrusions, enhancing its accuracy and responsiveness. This investigation aims
to strengthen the security framework of microgrids by integrating cutting-edge computational
methodologies, ensuring resilience against sophisticated cyber threats and fostering reliable network
operations.

This study not only focuses on detecting cyber threats but also emphasizes the adaptability and
scalability of the proposed model in real-world microgrid environments. By continuously analyzing
network traffic patterns, the system can evolve to recognize emerging attack strategies, making it a
proactive defense mechanism rather than a reactive one. Furthermore, the integration of multiple
machine learning techniques enhances the robustness of the detection framework, reducing false
positives and improving overall efficiency. As microgrids become increasingly reliant on WSNs,
implementing such intelligent security solutions is crucial to maintaining operational stability and
safeguarding critical infrastructure from evolving cyber threats.

The goal of this investigation is to advance the security framework of wireless sensor networks in
microgrids by integrating cutting-edge machine learning techniques. With a focus on maximizing
detection accuracy, the project aims to enhance cyber-attack identification, providing grid operators
with a powerful, automated tool that can assist in threat mitigation and ensure the resilience of
critical infrastructure. By leveraging a diverse set of classification algorithms, the model effectively
distinguishes between normal and malicious network behavior, minimizing false positives. This
approach not only strengthens the overall security posture of microgrids but also enables real-time
anomaly detection for swift incident response. Ultimately, the study contributes to the development
of intelligent cybersecurity solutions, ensuring the reliability and stability of next-generation smart
energy systems.

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LIST OF FIGURES
FIG. NO. FIGURE NAME PAGE NO.
4. 1 ARCHITECTURE DIAGRAM 12
4. 2. 1 DFD LEVEL - 0 14
4. 2. 2 DFD LEVEL - 1 15
4. 2. 3 DFD LEVEL - 2 16
4. 3. 1 USECASE DIAGRAM 18
4. 3. 2 CLASS DIAGRAM 19
4. 3. 3 SEQUENCE DIAGRAM 21
4. 3. 4 COMPONENT DIAGRAM 22
4. 3. 5 ACTIVITY DIAGRAM 23
4. 3. 6 DEPLOYMENT DIAGRAM 24
4. 4. 1 CONVOLUTIONAL NEURAL NETWORKS 25
4. 4. 2 PASSIVE AGGRESSIVE CLASSIFIERS 25
4. 4. 3 RANDOM FOREST CLASSIFIERS 26
4. 4. 4 XBOOST CLASSIFIERS 27

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LIST OF ABBREVIATIONS
S. No. Abbrevation Full Form
1 WSNs Wireless Sensor Networks
2 AI Artificial Intelligence
3 CNN Convolutional Neural Networks
4 NLP Natural Language Processing

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CHAPTER 1
INTRODUCTION
In today’s increasingly interconnected energy landscape, wireless sensor networks (WSNs)
play a pivotal role in the efficient management and distribution of renewable energy within
microgrids. These networks enable real-time monitoring, data collection, and automated control
of energy systems, ensuring reliability and sustainability. However, as microgrids continue to
expand, their growing reliance on WSNs makes them highly vulnerable to sophisticated cyber
threats. Attacks such as grayhole, blackhole, and flooding can disrupt energy distribution,
compromise system stability, and lead to significant operational and financial consequences.

In today’s digital era, the integration of Wireless Sensor Networks (WSNs) in microgrids
has revolutionized the way energy is managed, distributed, and monitored. These networks
enable real-time data collection, intelligent decision-making, and automation, ensuring that
renewable energy sources such as solar and wind power are efficiently utilized. As microgrids
become the backbone of modern energy infrastructure, their growing reliance on IoT-based
communication and control systems has made them susceptible to sophisticated cyber threats.
Cyber-attacks targeting WSNs can disrupt power distribution, compromise system stability, and
lead to financial and operational losses. The increasing frequency and severity of cyber threats
highlight the urgent need for robust and intelligent security mechanisms to safeguard these
critical infrastructures.

The rapid evolution of energy distribution systems has led to the widespread adoption of
Wireless Sensor Networks (WSNs) in microgrids, playing a pivotal role in monitoring, control,
and optimization. These WSNs enable real-time data collection and transmission, allowing
microgrids to efficiently manage distributed energy resources, balance supply and demand, and
enhance sustainability. However, as these networks expand in functionality and connectivity,
they become increasingly vulnerable to cyber threats that can disrupt operations, manipulate
critical data, and compromise the reliability of energy distribution. Cyber-attacks such as
grayhole, blackhole, and flooding attacks pose significant risks by targeting network
communication, injecting malicious packets, or overwhelming system resources. If left
undetected, these threats can lead to substantial financial losses, operational downtime, and
even large-scale power outages.

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To address these security challenges, this study introduces a robust machine learning-based
cyber-attack detection model specifically designed for WSNs in microgrids. By leveraging a
diverse set of machine learning algorithms, including Convolutional Neural Networks (CNN),
Passive Aggressive Classifiers, Random Forest Classifiers, and XGBoost Classifiers, the model
is capable of accurately distinguishing between normal network behavior and potential cyber
intrusions. The integration of these advanced classification techniques enhances the model's
ability to detect anomalies in network traffic, identify attack signatures, and mitigate threats
before they cause severe damage. Unlike traditional security measures, which often rely on
predefined rule sets and static detection mechanisms, the proposed model employs a dynamic
and adaptive approach, continuously learning from new attack patterns and improving detection
accuracy over time.

The implementation of this model involves the utilization of a comprehensive dataset from
Kaggle, which encompasses a diverse range of network traffic patterns, attack scenarios, and
normal operational states. By training the model on this dataset, it achieves high precision and
recall in detecting different types of cyber-attacks, ensuring minimal false positives and false
negatives. Moreover, the model’s real-time monitoring capabilities allow for proactive threat
identification, reducing response time and enabling microgrids to swiftly implement
countermeasures. The fusion of multiple machine learning techniques provides a well-rounded
defense mechanism, combining the interpretability of decision trees, the adaptability of online
learning algorithms, and the feature extraction capabilities of deep learning models.

Beyond its technical efficacy, the adoption of machine learning-driven cyber security
solutions in microgrids has profound implications for the future of energy management. Secure
and resilient microgrids play a crucial role in promoting renewable energy integration, reducing
carbon footprints, and ensuring energy independence. With the increasing deployment of smart
grids and decentralized power systems, safeguarding WSNs against cyber threats becomes not
only a technical necessity but also a strategic imperative. By implementing intelligent attack
detection mechanisms, energy providers can enhance grid security, prevent service disruptions,
and maintain the trust of consumers and stakeholders.Here are some key aspects of the future
scope:
1. Adaptive Threat Intelligence: The integration of artificial intelligence and machine learning
enables WSN-based microgrid security systems to continuously evolve by learning from
emerging cyber threats. Future implementations can incorporate self-learning AI models that
dynamically adapt to new attack patterns, ensuring long-term security and resilience.

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2. Real-Time Anomaly Detection: Advanced cybersecurity models can be integrated with real-
time monitoring systems, enabling faster threat identification and immediate response. By
deploying edge computing solutions, security mechanisms can process network data at the edge,
minimizing latency and enhancing detection efficiency.

3. Blockchain-Based Security Solutions: The combination of blockchain technology with cyber-

attack detection models can further strengthen microgrid security by enabling decentralized,
tamper-proof transaction records. Smart contracts can be utilized to automate security protocols
and verify the authenticity of network communications.

4. Integration with IoT and Smart Infrastructure: As smart cities and industrial automation
continue to expand, the need for secure energy management solutions grows. The proposed
machine learning-based security model can be adapted for broader IoT applications, enhancing
the overall cybersecurity of interconnected infrastructures.

5. Cross-Platform Compatibility and Scalability: Future cyber-attack detection systems must

be scalable and compatible with various microgrid configurations, including hybrid energy
systems, distributed power plants, and remote off-grid installations. Standardized security
frameworks will facilitate seamless integration across diverse energy ecosystems.

6. Ethical AI and Regulatory Compliance: As AI-driven cybersecurity solutions become more

prevalent, ensuring ethical AI practices and compliance with international cybersecurity
standards is crucial. Regulatory frameworks should be established to govern the responsible
deployment of machine learning models in critical infrastructure.

7. Automated Response and Mitigation Strategies: Future cybersecurity models can

incorporate automated threat mitigation techniques, enabling microgrids to autonomously
counteract attacks without human intervention. Automated incident response mechanisms can
isolate compromised nodes, reroute energy flow, and initiate recovery protocols.

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CHAPTER 2
LITERATURE SURVEY

A literature survey for "A Machine Learning Based Cyber Attack Detection Model For Wireless
Sensor Networks In Microgrids” involves exploring existing research, publications, and
advancements in the fields of cybersecurity, machine learning, wireless sensor networks
(WSNs), and microgrid security. The following summarizes key findings from relevant research
papers and publications:

[1] Kwon, Cheolhyeon, Weiyi Liu, and Inseok Hwang. (IEEE, 2013)
Security analysis for cyber-physical systems against stealthy deception attacks.
This study investigates security vulnerabilities in the state estimation problem within networked
control systems (NCS). The research highlights that communication channels between sensors
and remote estimators in NCS are highly susceptible to malicious adversaries, making them
prime targets for stealthy deception attacks. The findings emphasize the need for robust security
mechanisms to detect and mitigate these threats effectively.

[2] Pajic, Miroslav, James Weimer, Nicola Bezzo, Oleg Sokolsky, George J. Pappas, and
Insup Lee. (IEEE, 2017)
Design and implementation of attack-resilient cyber-physical systems: With a focus on attack-
resilient state estimators.This paper discusses the rising security threats in control systems
and provides an in-depth analysis of attack-resilient cyber-physical systems (CPS). It
references real-world cyberattacks, such as the Maroochy Water breach, illustrating the
consequences of compromised security in critical infrastructure. The authors propose attack-
resilient state estimators to safeguard control systems against malicious disruptions.

[3] Zeng, Wente, and Mo-Yuen Chow. (IEEE, 2014)

Resilient distributed control in the presence of misbehaving agents in networked control
systems.This research focuses on achieving consensus among agents in networked control
systems (NCS) even when some agents behave maliciously. The study introduces a reputation-
based resilient distributed control algorithm, which enhances the system's ability to function
despite adversarial agents. The proposed model ensures that a leader-follower consensus
network remains stable and operational under attack conditions.

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[4] Resilient control of networked control systems with stochastic denial-of-service (DoS)
attacks.
This paper addresses the challenge of securing networked control systems (NCS) against denial-
of-service (DoS) attacks. It models packet dropouts as a Markov process, allowing the system to
predict and adapt to attack patterns. The study presents a game-theoretic approach, balancing
attack strategies and defense mechanisms to enhance system resilience and maintain operational
stability.

[5] Ramasubramanian, P., Vijayakumar V., and S. C. Sharma. (IEEE, 2019)

Enhancing security in microgrids using machine learning-based attack detection.
This research investigates the use of machine learning techniques for cyber-attack detection in
microgrids. The authors focus on identifying abnormal behavior that may indicate an attack, such
as false data injection (FDI) or spoofing. The study introduces an innovative model combining
decision trees and neural networks to detect attacks with high efficiency. Results show significant
improvements in detection rates, reducing the risk of energy disruption and enhancing the
reliability of microgrids.

[6] Kumar, R., M. Gupta, and N. Agrawal. (Springer, 2020)

Anomaly detection in wireless sensor networks of smart grids in India using machine
learning.
In this research, the authors propose an anomaly detection system based on machine learning
techniques to secure wireless sensor networks (WSNs) used in smart grids. The study focuses
on the unique challenges faced by the Indian smart grid infrastructure, such as poor
communication infrastructure and intermittent power supply. The proposed system uses
Random Forest and K-means clustering algorithms to identify abnormal behavior in the
network, which could indicate a potential cyber attack. The results demonstrate the model’s
effectiveness in detecting attacks like blackhole and grayhole attacks, which are commonly
targeted at WSNs in energy systems.

[7] Sarma, A. K., and S. R. V. Tiwari. (Elsevier, 2017)

Application of machine learning for intrusion detection in wireless sensor networks of
microgrids.This paper investigates the application of machine learning for intrusion detection
in wireless sensor networks deployed within microgrids in India. The authors focus on energy
distribution systems in remote and rural areas of India, where microgrids play a crucial role in
ensuring continuous power supply.

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Using algorithms like SVM (Support Vector Machine) and k-NN (k-Nearest Neighbors), the
study proposes a hybrid detection model capable of identifying and classifying a variety of
cyber-attacks, including DoS and grayhole attacks. The model significantly enhances the
security of microgrids and improves the resilience of energy systems in these areas.

[8] Verma, A., and P. B. P. Reddy. (IEEE, 2019)

Energy security and cyber-attack detection in wireless sensor networks for microgrids in
India.
This research addresses the rising threats of cyber-attacks on wireless sensor networks in the
Indian microgrid systems. The study explores the impact of various cyber-attacks on the energy
distribution networks and proposes an advanced machine learning-based model for attack
detection. Using ensemble learning techniques, the authors develop a system that combines
multiple classifiers, including Random Forest and Gradient Boosting, to improve detection
accuracy. The study demonstrates that the model significantly enhances the energy security of
microgrids, especially in remote regions of India, by providing early detection and response
capabilities.

In conclusion, the development of a machine learning-based cyber-attack detection model for

wireless sensor networks (WSNs) in microgrids is a rapidly evolving field, driven by the
growing need for enhanced security in critical infrastructure. Ongoing research focuses on
improving detection accuracy, reducing false positives, and ensuring real-time performance in
dynamic environments. A comprehensive literature survey should incorporate the latest studies
from cybersecurity, WSNs, and microgrid management journals to provide a deep
understanding of emerging techniques, challenges, and solutions in safeguarding these systems
against cyber threats.

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CHAPTER 3
SOFTWARE REQUIREMENT ANALYSIS

3.1. PROBLEM STATEMENT

The critical problem addressed in this study revolves around the vulnerability of wireless sensor
networks (WSNs) within microgrids to various sophisticated cyber-attacks, such as grayhole,
blackhole, and flooding attacks. These attacks can severely disrupt the operation of microgrids,
leading to significant operational and financial losses. Traditional security mechanisms often
fall short in effectively detecting and mitigating such attacks due to their complexity and the
dynamic nature of cyber threats. Therefore, there is a pressing need for an advanced, efficient,
and accurate cyber attack detection model specifically designed for WSNs in microgrids,
leveraging the capabilities of machine learning algorithms to enhance the resilience and security
of these critical infrastructure systems.

3.2. MODULES AND THEIR FUNCTIONALITIES

To build an effective system for A Machine Learning-Based Cyber Attack Detection Model
for Wireless Sensor Networks in Microgrids, the project is divided into several modules, each
addressing a specific function within the overall workflow.
• User
The user allows users to interact with the Cyber Attack Classification application. This
module enables users to view the home page, access the about page, and load the dataset
required for modeling. It provides an intuitive interface for users to input necessary
values, view results, and assess the accuracy of the model's predictions.
• Data-Loading
The load data page in this module allows the user to upload the dataset that will be used
for training and testing the cyberattack classification model. This step ensures that the
application has the necessary data to begin the machine learning process.
• Input-Model
The input model section allows users to provide input values for specific fields required
for the model. Users must fill in relevant information, such as data parameters, for the
model to generate accurate results based on the provided data.

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• Results-Display
The view results module enables users to view the classification results generated by
the model. It provides insights into the predicted attack types, such as grayhole,
blackhole, or flooding attacks, offering clear information on the detected threats.
• Accuracy-Score
The view score feature displays the accuracy percentage of the model’s prediction. This
allows users to evaluate how well the model is performing in terms of detecting cyber
attacks, providing valuable feedback on its effectiveness.
Each module is designed to work seamlessly together, providing an efficient and user-
friendly experience for identifying cyber threats in wireless sensor networks within
microgrids.

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CHAPTER 4
SOFTWARE DESIGN
4. 1 Architecture Diagram

Fig 4. 1 ARCHITECTURE DIAGRAM

The above figure describes the architecture design of the system as follows:

Input: Network traffic data from wireless sensor networks in microgrids is collected for analysis.

User Interaction: The user initiates the model-building process by providing relevant data.

Dataset: The system accesses a dataset containing normal and attack-related network traffic data.

Training: The system is trained using machine learning techniques to detect cyber attacks in
microgrids.

Data Splitting: Divides data into training and testing sets for model development.

Training (CNN & MobileNet): Uses CNN and MobileNet models to learn patterns in medical
images.

Model Building: The trained system builds a model to classify network traffic as normal or
malicious.

Testing: The built model is tested using unseen network data to evaluate its accuracy and reliability.

Evaluation: The model's performance is measured using accuracy, precision, recall, and F1-score.

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Output: The system provides a classification result indicating whether the network traffic is normal
or under attack, helping in proactive security measures.

4. 2. DATA FLOW DIAGRAMS

A Data Flow Diagram (DFD) is a graphical tool used to visualize the flow of information in a
system or process. It uses a standardized set of symbols to represent:
• Processes: Transformations that convert data from one form to another.

• Data Flows: Movement of data between processes, external entities, and data stores.

• External Entities: Sources and destinations of data outside the system.

• Data Stores: Places where data is kept for future use.

DFDs are helpful for:

• Understanding How a System Works: By following the data flow, you can see how
data is processed and transformed.

• Communicating System Requirements: DFDs are a clear and concise way to show
what data is needed and how it will be used.

• Designing New Systems: DFDs can be used to plan out the data flow of a new system
before it is built.

4. 2. 1. DFD LEVEL – 0

Level 0 (Context Diagram): A high-level overview of the entire system, showing its main
processes and external entities. The below Fig No. 4.2.1 shows a high-level overview of the
System. The system consists of four main components:

Fig 4. 2. 1 DFD LEVEL – 0

• Trainer/System: The module responsible for training the data

• User: The entity that provides data.

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• Process: The core system component that data and create models.
• Data Store: A storage unit where processed data, trained models, and medical records are kept
for future reference.

4. 2. 2. DFD LEVEL – 1

The below Fig No. 4.2.2 shows a high-level overview of the Cardiac Disease Prediction Using
Image Classification system.

Fig 4. 2. 2 DFD LEVEL – 1

Key Components:

• System: The main processing unit responsible for handling data, processing, and generating
predictions.
• User: The entity that interacts with the system by providing data and viewing results.
• Register: Users register for an account to access the system.

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• Login: Registered users sign in to the system.

• Upload Data: Users upload medical images and related data for processing.
• Take Data: The system collects and processes uploaded data.
• Pre-processing: The system prepares the data by filtering noise, normalizing, and
transforming it into a suitable format for model analysis.
• Model Building: A machine learning model (such as CNN/MobileNet) is trained using
medical images.
• Select Model: Users choose a trained classification model for prediction.
• Prediction: The system applies the selected model to classify medical images and detect
potential cardiac diseases.
• Generate Results: The classification results are processed, and a report is created.
• View Results: The final prediction results are displayed to the user.
• View Data: Users can access and review uploaded or processed data.
• Data Store: A storage unit that retains user information, trained models, medical images, and
classification results.

Data Flows:

• User to System: Users provide medical images and other relevant data as input.
• System to Pre-processing: The system processes the raw data to improve model accuracy.
• Pre-processed Data to Model Building: The processed data is used to train and optimize the
classification model.
• User to Model Selection: Users select a trained model for classification.
• Model to Prediction Module: The selected model classifies the uploaded medical images.
• Prediction Results to System: The system processes the classification results for reporting.
• Results to Data Store: The final results are stored in the database for future access.
• Data Store to View Results: Users retrieve and review prediction results from storage.

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4.2. 3. DFD LEVEL – 2

The below Fig No. 4.2.3 shows a high-level overview of the system.

Fig 4. 2. 3 DFD LEVEL – 2

Key Processes:
• Data Upload & Preprocessing: Users upload data, which is preprocessed before
being used in the model.

• Model Selection & Training: The system enables users to select a model and train it
using data.

• Prediction & Classification: Trained models classify data inputs and generate
predictions.

• Result Generation & Viewing: Users can generate, store, and view results from
predictions..

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Key Components:

• User: The end-user who interacts with the system for data upload, processing, and result
viewing.

• System: The core processing unit responsible for data handling, model training, and
prediction.

• Data Store: A centralized database that stores user data, trained models, and classification
results.

COMPONENTS:

1. Register: New users create an account.

2. Login: Registered users sign into the system.

3. Upload Data: Users upload data for processing.

4. Pre-processing: Data is refined and prepared for model training.

5. Model Building: The deep learning model is trained using the uploaded data.

6. Select Model: Users choose an appropriate trained model for prediction.

7. Take Data: System retrieves data for analysis.

8. Prediction: The selected model processes input data and makes predictions.

9. Generate Results: The system generates a classification report.

10. View Data & Results: Users can view stored data and prediction outcomes.

Data Flows:

1. User Uploads Data: Medical or relevant data is uploaded for analysis.

2. Data Pre-processing: The system prepares and processes the data for training.
3. Model Selection & Training: The user selects a model, which is then trained on the
processed data.
4. Data Sent to Prediction Model: The trained model is used for making predictions.
5. Prediction & Result Generation: The model classifies the input data and generates
reports.
6. Results Stored & Displayed: Classification results are stored in the database and made
available for user viewing.

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4. 3. UML DIAGRAMS
4. 3. 1. USE CASE DIAGRAM

A use case diagram at its simplest is a representation of a user's interaction with the system,
depicting the specifications of a use case. The below Fig No. 4.3.1 portrays the different types of
users of the Cardiac Disease Prediction System and the various ways they interact with the system.

Fig 4. 3. 1 USE CASE DIAGRAM

ACTORS:

1. User: The primary actor interacts with the system by uploading network traffic data for
cyber attack detection.

2. System: Represents the Cyber Attack Detection System, responsible for data processing,
attack detection, and result generation.
USE CASES:
1. Register: The user creates an account to access the system.

2. Login: The user logs in to the system to perform operations.

3. Upload Data: The user uploads network traffic data for analysis.

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4. Take Data: The system retrieves the uploaded data for processing.

5. View Data: The user can view and analyze the uploaded network data.

6. Pre-processing: The system processes the raw data by cleaning, normalizing, and preparing
it for model training.

7. Model Building: The system trains a machine learning model to detect cyber attacks in the
network traffic.
8. Select Model: The user selects a preferred model for attack detection.

9. Prediction: The system predicts whether the network traffic is normal or under cyber attack.

4. 3. 2. CLASS DIAGRAM
The class diagram is the main building block of object-oriented modeling. It is used both for
general conceptual modeling of the systematics of the application and for detailed modeling,
translating the models into programming code. In the diagram, classes are represented with boxes
that contain three parts:
• The upper part holds the name of the class.
• The middle part contains the attributes of the class.
• The bottom part gives the methods or operations the class can take or undertake.

Fig 4. 3. 2 CLASS DIAGRAM

The above Fig No. 4.3.2 shows the main components of the system and how they interact with each
other.

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The main components are:

1. Data Processing:
o This component is responsible for taking the data input from users.
o It includes pre-processing steps such as data cleaning, normalization, and
transformation to ensure high-quality input for the model.
2. Model Building:
o This method applies machine learning techniques to train a model for detecting cyber
attacks.
o It learns patterns from historical network traffic data to improve accuracy in identifying
threats..
3. Prediction:
o This component takes the trained model and applies it to new network traffic data.
o It predicts whether the network traffic is normal or under attack, classifying different
types of cyber threats.
4. Result Generation:
o This class generates and displays the results based on the prediction..
o It provides insights that help users assess network security and take appropriate actions.

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4. 3. 3. SEQUENCE DIAGRAM

A sequence diagram is a kind of interaction diagram that shows how processes operate with one
another and in what order. It is a construct of a Message Sequence Chart. A sequence diagram
shows object interactions arranged in a time sequence. Sequence diagrams are sometimes called
event diagrams, event scenarios, and timing diagrams.

Fig 4. 3. 3 SEQUENCE DIAGRAM

The above Fig No. 4.3.3 shows the interaction between the user and the System

1. User:

• The user first registers on the system.

• After successful registration, the user logs in to access the system functionalities.

• The user can view existing data related to cyber attack detection..

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2. Data Upload & Processing:

• The user uploads relevant network traffic data.

• The system takes the uploaded data for further analysis.

• Pre-processing steps are applied to clean and structure the data for training and
prediction.

3. Model Training & Selection:

• The system builds a machine learning model using the pre-processed data.

• The user selects the appropriate model for cyber attack detection.

4. Prediction & Result Generation:

• The selected model is used for predicting potential cyber attacks.

• The system generates results based on the prediction and analysis.

4. Finally, the user can view the detection results, gaining insights into the security status of
the wireless sensor network in the microgrid.

5. 3. 4. COMPONENT DIAGRAM

The below Fig No. 4.3.4 depicts how components are wired together to form larger components
and/or software systems for our project. This diagram illustrates the structure of arbitrarily
complex systems, showing the interaction between the Trainer/System and the User.

Fig 4. 3. 4 COMPONENT DIAGRAM

User: This component represents the person who is using the system.
System: Comprises the following sub-components:
• Data Processing: Takes user-provided network data, cleans, and prepares it for analysis.
• Feature Extraction: Identifies critical patterns and anomalies indicative of potential cyber
attacks.

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• Attack Detection: Applies machine learning models to classify whether the n

Output:

• Displays the classification results, attack likelihood, and security recommendations.

• Provides a detailed analysis of detected anomalies in the wireless sensor network of the
microgrid.

4.3. 5. ACTIVITY DIAGRAM

The activity diagram is another important diagram in UML to describe the dynamic aspects of the
system. It is a flowchart that represents the flow from one activity to another.An activity can be
described as an operation of the system, as shown in Fig No. 4.3.5.

Fig 4. 3. 5 ACTIVITY DIAGRAM

User :
• Start: The user begins by accessing the system.
• View Homepage: The user interacts with the homepage to navigate the system.
• Load Homepage: The system loads the homepage interface for the user.
• Select Model: The user selects a machine learning model for analysis.

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• View Results: Once processing is complete, the user views the results generated by the
system.
System :
• Working on Data: The system begins processing the received data.
• Preprocessing: The system cleans, normalizes, and prepares the data for analysis.
• Training the Data: The system trains the selected machine learning model using relevant
datasets.
• Model Building: The system builds and fine-tunes the model for accurate predictions.
• Generate Results: The system processes the final output and generates results based on the
trained model.
4.3. 6. DEPLOYMENT DIAGRAM

A deployment diagram in the Unified Modeling Language models the physical deployment of
artifacts on nodes, which is shown in Fig No. 4.3.6 for our project.

Fig 4. 3. 6 DEPLOYMENT DIAGRAM

User: This represents the person or entity interacting with the Machine Learning-Based Cyber Attack
Detection Model for Wireless Sensor Networks in Microgrids. The user provides input data related to
network traffic, sensor behavior, or other security-related parameters to detect cyber threats.
System: This is the core component responsible for analyzing sensor data and predicting cyber attacks
based on the user’s input. It consists of multiple sub-components:
• Data Preprocessing: Cleans and organizes the input data, ensuring it is ready for analysis.
• Feature Extraction: Identifies crucial features related to cyber attack patterns within sensor
networks.
• Model Classification: Uses machine learning algorithms (e.g., CNN, Passive Aggressive
Classifier) to detect anomalies or potential cyber threats.
• Result Generation: Provides the final classification output, highlighting whether a cyber attack
has been detected and offering insights for further security measures.

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4.4 METHODOLOGY
4.4.1 CONVOLUTIONAL NEURAL NETWORKS

Convolutional Neural Network (CNN) Convolutional Neural Networks (CNNs) are a fundamental
deep learning architecture specifically designed for processing structured grid data, such as images.
They excel in tasks like image classification, object detection, and feature extraction by leveraging
hierarchical feature learning.

A key strength of CNNs is their ability to automatically extract relevant features from raw data through
convolutional and pooling operations. These layers enable the model to detect local features and spatial
relationships, making CNNs highly effective in recognizing intricate patterns within images. Unlike
traditional machine learning approaches that require manual feature engineering, CNNs learn and
refine features directly from data, improving performance and reducing human intervention.

The adaptability of CNNs to varying input sizes further enhances their utility, allowing them to process
images with different resolutions while maintaining performance. This feature is particularly valuable
in fields such as medical imaging and cybersecurity, where datasets may contain diverse image sizes
and formats. By capturing spatial hierarchies and dependencies, CNNs have become a pivotal tool in
computer vision applications, enabling state-of-the-art performance in tasks requiring deep feature
understanding.

Fig 4. 4. 1 CONVOLUTIONAL NEURAL NETWORK

4. 4. 2 PASSIVE AGGRESSIVE CLASSIFIERS

Passive Aggressive classifiers are a type of online learning algorithm specifically designed for binary
classification tasks. Unlike traditional batch learning methods that require the entire dataset to be
available before training, Passive Aggressive classifiers update their model parameters sequentially
with each new instance of data.

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This makes them particularly effective in scenarios involving streaming data, where information
arrives continuously, and quick adaptation is crucial. These classifiers strike a balance between
maintaining previously learned patterns and incorporating new insights, ensuring their decision
boundaries remain relevant as data evolves.

The working principle of Passive Aggressive classifiers is based on adjusting their decision boundary
only when a misclassification occurs. However, when an incorrect classification is made, the model
aggressively updates its parameters to correct the mistake while ensuring minimal changes to prevent
overfitting. The degree of aggressiveness in updates is controlled by a hyperparameter, which regulates
how much the model should adjust in response to misclassifications. This adaptability makes Passive
Aggressive classifiers highly effective in dynamic environments where data patterns shift frequently.

In summary, Unlike traditional batch learning models, they update their parameters incrementally, adapting
quickly to new data while requiring minimal memory. These classifiers remain passive when predictions are
correct but aggressively adjust their decision boundaries upon misclassification, with the degree of adjustment
controlled by a hyperparameter. This balance between adaptability and stability makes them suitable for dynamic
datasets where patterns continuously evolve. Due to their efficiency and real-time learning capabilities, Passive
Aggressive classifiers are widely used in applications such as text classification, spam detection, sentiment
analysis, and cybersecurity tasks like anomaly detection in network traffic. Their ability to handle continuous
data streams effectively makes them an essential tool for real-time decision-making.

4.4.3 RANDOM FOREST CLASSIFIERS

Random Forest classifiers are ensemble learning methods based on decision tree models.

Fig 4. 4. 2 RANDOM FOREST CLASSIFIERS

They operate by constructing a multitude of decision trees during training and outputting the mode of
the classes (classification) or mean prediction (regression) of individual trees.

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Each tree is trained on a random subset of the training data and features, promoting diversity among
the trees and reducing overfitting.
Random Forests are known for their robustness, scalability, and ability to handle high-dimensional data
with complex interactions. They are widely used for tasks like classification, regression, feature
importance ranking, and anomaly detection in various domains.

4.4.4 XGBOOST CLASSIFIERS:

XGBoost (Extreme Gradient Boosting) is a scalable and efficient implementation of gradient boosting
machines. It sequentially builds a ensemble of weak learners (typically decision trees) to minimize a
user-specified loss function.

Fig 4. 4. 3 XGBOOST CLASSIFIERS

XGBoost employs a regularization term in the objective function to control model complexity and
prevent overfitting. It utilizes a distributed and parallel computing framework, making it suitable for
large-scale datasets and computationally intensive tasks. XGBoost has gained popularity due to its
exceptional performance in various machine learning competitions and real-world applications,
including classification, regression, ranking, and anomaly detection.

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CHAPTER 5
SOFTWARE AND HARDWARE REQUIREMENTS
5.1. SOFTWARE REQUIREMENTS
• Operating System : Windows 7/8/10
• Server side Script : HTML, CSS, Bootstrap & JS
• IDE/Workbench : PyCharm/VSCode
• Framework : Flask, pandas, numpy and Scikit-Learn

5.2. HARDWARE REQUIREMENTS

• Processor - Intel 3rd generation or high or Ryzen with 8 GB Ram
• RAM - 8GB (min)
• Hard Disk - More than 500 GB
• Key Board - Standard Windows Keyboard
• Mouse - Two or Three Button Mouse
• Monitor - Any

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CHAPTER 6
CONCLUSION
In conclusion, the development of a machine learning-based cyber attack detection model for
wireless sensor networks in microgrids represents a significant step forward in securing critical
infrastructure. By leveraging advanced machine learning techniques, this system effectively
identifies and mitigates cyber threats, ensuring the stability and security of microgrid
operations.

The integration of AI-driven models in cybersecurity enhances real-time threat detection,

minimizes response times, and reduces the likelihood of system failures due to malicious
attacks. With wireless sensor networks playing a crucial role in monitoring and managing
microgrid performance, the ability to detect anomalies and cyber intrusions promptly is vital
for maintaining operational efficiency and resilience.

However, while AI-based detection models offer substantial benefits, human intervention
remains necessary for continuous monitoring, model refinement, and addressing evolving cyber
threats. The effectiveness of this approach depends on the quality and diversity of training
datasets, as well as the system’s adaptability to emerging attack patterns.

Looking ahead, further improvements can be made by incorporating federated learning,

blockchain security mechanisms, and adaptive AI models to enhance robustness. As cyber
threats become increasingly sophisticated, the ongoing evolution of AI-powered security
solutions will be crucial in safeguarding microgrid systems. This research underscores the
importance of integrating AI with cybersecurity strategies, paving the way for more resilient
and intelligent security frameworks in future energy networks.

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CHAPTER 7
REFERENCES
1. A. Kavousi-Fard, W. Su, and T. Jin (2020): A Machine-Learning-Based Cyber Attack
Detection Model for Wireless Sensor Networks in Microgrids, IEEE Transactions on
Industrial Informatics, vol. 17, no. 1, pp. 650–658. Available at:
https://ieeexplore.ieee.org/document/8951100
2. C. Zhang, L. Wu, Y. Wang, and X. Li (2023): Machine Learning-Based Cyber Attack
Detection in Wireless Sensor Networks for Microgrids, IEEE Transactions on Smart Grid.
3. A. Sharma, B. Patel, and D. Kim (2022): AI-Driven Intrusion Detection for Microgrid
Security: A Deep Learning Approach, International Journal of Smart Energy Systems.
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