Popular GitHub Repositories for Intrusion

Slips, a free software behavioral Python intrusion prevention system (IDS/IPS) that uses machine learning to detect malicious behaviors in the network traffic. Stratosphere Laboratory, AIC, FEL, CVUT in Prague.

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

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Machine Learning for Computer 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.

Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities

Code for intrusion detection system (IDS) development using CNN models and transfer learning

[ICMLC 2018] A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection

Network Intrusion Detection System using Deep Learning Techniques

Deep learning based object tracking with line crossing and area intrusion detection

Anomaly Detection on Time-Evolving Streams in Real-time. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

Machine Learning based Intrusion Detection Systems are difficult to evaluate due to a shortage of datasets representing accurately network traffic and their associated threats. In this project we attempt at solving this problem by presenting two taxonomies

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.

MemStream: Memory-Based Streaming Anomaly Detection

This tool parses log data and allows to define analysis pipelines for anomaly detection. It was designed to run the analysis with limited resources and lowest possible permissions to make it suitable for production server use.

Flash-IDS is an open-source system developed by the DART Laboratory for advanced intrusion detection using provenance graph representation learning. It implements the techniques presented in our IEEE S&P 2024 paper, "FLASH: A Comprehensive Approach to Intrusion Detection via Provenance Graph Representation Learning.

Sketch-Based Anomaly Detection in Streaming Graphs

Real-time Intrusion Detection System implementing Machine Learning. We combine Supervised Learning (RF) for detecting known attacks from CICIDS 2018 & SCVIC-APT datasets, and Unsupervised Learning (AE) for anomaly detection.

Real-time eBPF-powered network security monitor with AI-driven threat detection. Surfaces port scans, DDoS attacks, botnet activity, and anomalies at 100Gbps+ speeds with sub-microsecond latency (~150 million packets/sec).

Solutions to kdd99 dataset with Decision tree and Neural network by scikit-learn