Deep learning framework for network intrusion detection using CNN, RNN (LSTM), and Hybrid architectures on CIC-IDS2017.
Memory-augmented SOC alert-triage research prototype with reproducible CIC-IDS2017 validation, leakage-aware controls, and publication-ready figures.
Multi-class network intrusion detection on CIC-IDS2017 — 99.85% accuracy across 15 classes on 2.5M flows. Random Forest, XGBoost, KNN vs. three from-scratch Bayesian classifiers, with SFFS feature selection and multi-seed stability analysis.
Adversarial robustness in ML-based NIDS using CIC-IDS2017 (IITK B.Cyber project)
Strategic Business IT analysis for a lightweight machine learning-based Network Intrusion Detection System, including project concept, SWOT analysis, technology selection, legal and ethical issues, and competitive advantage.
Explainable intrusion detection on CIC-IDS2017 (2.8M flows). XGBoost at 99.65% accuracy with SHAP explanations for every prediction, including worked false-positive and false-negative case studies. MSc dissertation.
Deep Packet Inspection Engine with ML-powered Intrusion Detection using CIC-IDS2017 dataset
Detect network intrusions with deep learning using CNN, LSTM, and hybrid models on CIC-IDS2017 data.
To associate your repository with the cic-ids2017 topic, visit your repo's landing page and select "manage topics."