A Suricata based IDS/IPS/NSM distro

Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15

Scirius is a web application for Suricata ruleset management and threat hunting.

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities

Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset

Network intrusions classification using algorithms such as Support Vector Machine (SVM), Decision Tree, Naive Baye, K-Nearest Neighbor (KNN), Logistic Regression and Random Forest.

Repository for IEEE CCNC'21 paper titled "Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network".

Network Intrusion Detection System Project using Machine Learning with code and Documents

Network Intrusion Detection System

En este proyecto se evalúan y comparan diferentes técnicas de aprendizaje automático para la detección de intrusiones en red.

Implemented a network intrusion detection system for a software defined network using Random Forest method for classification of port and flow statistics.

Payload-Byte is a tool for extracting and labeling packet capture (Pcap) files of modern network intrusion detection datasets.

Cyber Security: Development of Network Intrusion Detection System (NIDS), with Machine Learning and Deep Learning (RNN) models, MERN web I/O System.

Neural Network based Intrusion Detection System (NIDS) on Intrusion Detection Evaluation Dataset (CICIDS2017)

The given information of network connection, model predicts if connection has some intrusion or not. Binary classification for good and bad type of the connection further converting to multi-class classification and most prominent is feature importance analysis.

Assess various ML algorithms on KDD99 network dataset then apply the best algorithm (Random Forest) using R.

Analyzes network traffic and tells whether the query is normal or a type of attack. 3 classifiers are built and tested: Naive Bayes, Decision Trees, Random Forests, Followed by a complete visualization of results

Network Intrusion Detection in an Adversarial setting