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。

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

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

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

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.

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

Anomaly IDS using a one-class autoencoder.

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

This project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats

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

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