Popular GitHub Repositories for Intrusion Detection System

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.

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.

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.

IoT intrusion Detection Model based on neural network and random forests

Machine Learning Based - Intrusion Detection System

Adversarial Machine Learning applications on network-based Intrusion Detection System (IDS).

Network related services, programs and applications are developing greatly, however, network security breaches are also developing with them. Network security is an evolving, challenging and a critical task. It is essential that there is a system in place to identify any harmful movement happening in network. An Intrusion detection system (IDS) has become the prerequisite software addressing cyber security in the modern era. Especially, with the greater complexity of advanced cyber-attacks and as such the uncertainty surrounding the detection of the types of attacks. This thesis proposes a novel approach using an ensemble of K-Means and Gaussian Mixture clustering combined with a deep neural network (DNN) algorithm. When compared with traditional artificial neural network’s (ANN’s) used within an IDS, our approach implements modern advanc

Federated Learning for Intrusion Detection System using the Flower framework and UNSW_NB15 dataset.

Network Intrusion Detection System on CSE-CIC-IDS2018 using ML classifiers and DNN ( ANN , CNN , RNN ) | Hyper-parameter Optimization { learning rate, epochs, network architectures, regularisation } | Adversarial Attacks - Label flip , Adversarial samples , KNN (defence)

Intrusion detection engine for Cloud Systems built using Alternative Fuzzy C-mean Clustering and Artificial Neural Network

:computer: Network System Intrusion Detection using Graph Neural Networks

Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO

Official repository for the paper "Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks"

On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems

Intrusion Detection System that uses Machine Learning to detect insider threat & insider anomaly behavior with the help of machine learning on everyday tasks and more.

A Federated Learning based approach to combat heterogeneity of attacks on IoT devices.

Optimizing Smart Home Intrusion Detection with Harmony-Enhanced Extra Trees

This project lays the groundwork for potentially improving anomaly detection accuracy by exploring the feasibility of embedding a dynamic ensemble selection classifier layer within the neural networks.

The "Intrusion-Detection-System-Using-ML" repository by SudoAnirudh features a machine learning-based Network Intrusion Detection System with a Flask web interface. It includes components such as machine learning models, datasets, and static files for detecting network intrusions.