cic-ids2017

Deep learning framework for network intrusion detection using CNN, RNN (LSTM), and Hybrid architectures on CIC-IDS2017.

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.

Memory-augmented SOC alert-triage research prototype with reproducible CIC-IDS2017 validation, leakage-aware controls, and publication-ready figures.

Adversarial robustness in ML-based NIDS using CIC-IDS2017 (IITK B.Cyber project)

Explainable hybrid IDS with leakage-aware cross-dataset evaluation on UNSW-NB15, CIC-IDS2017, and ToN-IoT.

Explainable ML intrusion detection on CIC-IDS2017 under leakage-aware evaluation

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.

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.

• Developed a Flask-based Network Intrusion Detection System using Random Forest and CIC-IDS2017, classifying network traffic across 78 features with 99.65% test accuracy. • Implemented manual/CSV prediction, confidence scoring, attack categorization, prediction history, downloadable reports, and an interactive monitoring dashboard.

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.