Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

Data stream analytics: Implement online learning methods to address concept drift and model drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" published in IEEE GlobeCom 2021.

A thesis submitted for the degree of Master of Science in Computer Networks and Security

An Intrusion Detection System based on Deep Belief Networks

CICIDS2017 dataset

Data stream analytics: Implement online learning methods to address concept drift and model drift in dynamic data streams. Code for the paper entitled "A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems" published in IEEE Transactions on Industrial Informatics.

This repository contains an in-depth analysis of the Intrusion Detection Evaluation Dataset (CIC-IDS2017) for Intrusion Detection, showcasing the implementation and comparison of different machine learning models for binary and multi-class classification tasks.

This repository contains my third year dissertation. My dissertation focused in evaluating and creating a DNN for a Network Intrusion Detection System (NIDS).

Attack Detection, Parameter Optimization and Performance Analysis in Enterprise Networks (ML Networks) for Intrusion Detection System IDS.

ThreatGuard is an advanced threat detection system that utilizes the CICIDS 2017 dataset for network traffic analysis and anomaly detection.

An Advanced IDS with HoneyPot Fusion for Proactive Threat Mitigation and Detection; Research Project