A Suricata based NDR distribution

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.

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

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

Network Intrusion Detection System

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

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

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.

GNN4ID: A Toolset for Crafting Graph Neural Network-Based NIDS Datasets

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

Cyber Security: Development of Network Intrusion Detection System (NIDS), with Machine Learning and Deep Learning (RNN) models, MERN web I/O System. The deployed project link is as follows.

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

The Unified Multimodal NIDS Dataset Tool performs the standardization of network intrusion detection datasets by extracting comprehensive flow, payload, and contextual features from raw PCAP files, ensuring consistency across datasets and enhancing machine learning-driven threat detection and analysis.