Current Project
Intrusion Detection System with ML & Deep Learning
A hybrid intrusion detection system combining ensemble machine learning and deep learning techniques for enhanced cybersecurity.
Status
Completed
Sector
Cybersecurity & AI
Stack
08
Project Overview
This project evaluates and integrates multiple ML techniques including bagging, boosting, and CNN-based deep learning for detecting network intrusions.
Tech Stack
Challenge
Traditional IDS systems struggle with high false positives and evolving cyber threats.
Delivery
A comparative framework with optimized ensemble models and deep learning pipelines for intrusion detection.
Impact
⭐⭐⭐⭐⭐ Reduced false positive rates Improved detection accuracy Robust cybersecurity framework Scalable threat detection system
Project Highlights
Highlight 01
Ensemble learning (Bagging & Boosting)
Highlight 02
CNN-based intrusion detection
Highlight 03
Comparative performance analysis
Highlight 04
Feature engineering and selection
Highlight 05
Real-time detection pipeline
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