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

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

Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities

A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach

This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets

An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" published in IEEE Internet of Things Magazine.

这是一个封装了KDDCup99、NSL-KDD、UNSW-NB15等入侵监测数据集的Python包。

NSLKDD Dataset for WEKA

This repository contains a notebook implementing an autoencoder based approach for intrusion detection, the full documentation of the study will be available shortly.

Cyber-attack classification in the network traffic database using NSL-KDD dataset

IDS based on Machine Learning technical

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

Anomaly IDS using a one-class autoencoder.

Scripts for downloading, preprocessing, and numpy-ifying popular machine learning datasets

A comparison between Statistical, Machine Learning, PCA, SVD, and REF methods

Codes for the paper entitled "Optimization of Predictive Performance of Intrusion Detection System Using Hybrid Ensemble Model for Secure Systems"

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

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

Detedcting attacks in intrusion detedtion using RNN

A network Intrusion Detection System (IDS) based on Self-Organizing Neural Networks (SOINN).