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

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

A Framework for DDoS Attack Detection Using Hyperparameter Optimization, WGAN-GP–Based Data Augmentation, Feature Extraction via Genetic Algorithms, RFE, and PCA, with Individual and Ensemble Classifiers for Multi-Dataset Evaluation.

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

Network Intrusion Detection System using Machine Learning and Deep Learning

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

Unsupervised anomaly detection on CIC-IDS 2017 (Monday BENIGN traffic) using Isolation Forest and PCA visualization — complete preprocessing and EDA included.

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.