Completed Project
Comparative Analysis of ML Techniques for Intrusion Detection Systems
A machine learning and deep learning project for detecting network intrusions using ensemble methods and CNN.
Status
Delivered
Sector
Cybersecurity / Machine Learning
Stack
08
Project Overview
This project presents a comparative study of traditional machine learning models, ensemble techniques, and deep learning (CNN) for intrusion detection. Using NSL-KDD and CICIDS2017 datasets, the system evaluates model performance and identifies the most effective approach for detecting cyber threats in network traffic.
Tech Stack
Challenge
Handling large-scale datasets, class imbalance, noisy data, and computational limitations during model training.
Delivery
Developed a complete IDS pipeline including preprocessing, model training, evaluation, and comparative analysis across multiple algorithms.
Impact
Improved intrusion detection accuracy and demonstrated that ensemble and deep learning models significantly outperform traditional approaches.
Project Highlights
Highlight 01
Built full end-to-end IDS pipeline
Highlight 02
Compared ML, Ensemble, and CNN models
Highlight 03
Achieved high detection accuracy with XGBoost
Highlight 04
Implemented feature engineering and selection
Highlight 05
Performed advanced model validation
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