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

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

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.

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.

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 includes code for the paper "Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks" published in IEEE TCOM, focusing on autonomous cybersecurity (physical-layer authentication and cross-layer intrusion detection) using AutoML techniques.

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.

Network-Based Intrusion Detection System - dev/deploy-ment

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

Intrusion Detection System (IDS): An open-source system for real-time network monitoring and threat detection.

Deep learning model comparison for intrusion detection on CIC-IDS2017 dataset

Building a Machine Learning-based NIDS using XGBoost trained on the CICIDS2017 dataset. 🚀

Anomaly-Based Network Intrusion Detection Using Ensemble Learning

Hybrid AI-powered Intrusion Detection System (NIDS) combining 1D-CNN & Variational Autoencoder (VAE) to detect known cyberattacks and zero-day anomalies. Features a premium Streamlit "Command Center" dashboard for real-time network traffic analysis.

Real-time IDS using Raspberry Pi + Machine Learning trained on CICIDS2017 dataset. Live packet capture, flow extraction, REST API alerts, and dashboard.

🚀 Detect network intrusions with this ML-based system using the NSL-KDD dataset. Features include model training, API deployment, and an interactive dashboard.

Network Intrusion Detection System using Machine Learning and Deep Learning