Enhancing Network Intrusion Detection: An Online Methodology for Performance Analysis

Intrusion Detection

Welcome to my Individual Project for Codeup: Network Intrusion Detection. The goal was to build a machine learning model to detect anomalous behavior. Data courtesy of Kaggle.

The objective of the project is to build network intrusion detection system to detect anomalies and attacks in the network.

Applying machine learning algorithms to classify military network connections as normal or anomalous.

Cyber Security: Development of Network Intrusion Detection System (NIDS), with Machine Learning and Deep Learning (RNN) models, MERN web I/O System.

This project demonstrates end-to-end development of Network Intrusion Detection from dataset, model training to GUI using Flask. So its a complete end-to-end application..

Regression , Classification and Segmentation projects

Utilizing Generative AI coupled with Deep Neural Networks to classify network intrusions from the widely recognized NSL-KDD dataset and is based on a research paper I produced in Spring 2024 with the help of a few others listed below.

The project aims to design and develop a full-stack network intrusion detection system using machine learning techniques. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

VigiNIDS: A machine learning-based system for detecting malicious network traffic using the UNSW-NB15 dataset. It distinguishes between normal and attack activities, providing a data-driven approach to network security.

Network Intrusion Detection System

A python-based Network Intusion Detection System, for every one.

Final Year Project Topics For Computer Engineering Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Research-level implementation of unsupervised anomaly detection using KMeans, DBSCAN, Isolation Forest, and deep Autoencoders. Applied to IoT sensors, financial fraud, network intrusion, and time-series fault detection. Built for PhD-oriented ML portfolios.

Network-Intrusion-Detection

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs

A complete pipeline for network intrusion detection comparing label encoding and one‑hot encoding, with SMOTE resampling, feature selection, and ensemble modeling using scikit‑learn and XGBoost, also this was phase one of our University's "CSAI 253- Machine Learning" course.