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.

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

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

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.

IDS based on Machine Learning technical

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

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

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

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 (RNN) models, MERN web I/O System.

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

🛡️ Generate synthetic data for intrusion detection systems using GANs to improve performance on NSL-KDD and UNSW-NB15 datasets.

Multi-class attack detection on NSL-KDD dataset using TabTransformer

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

The design and implementation of an advanced BiLSTM-based model integrated with an attention mechanism for network intrusion detection using the NSL-KDD dataset.

Detedcting attacks in intrusion detedtion using RNN

A Random Forest model that detects network intrusion and anomalies, using the NSL-KDD dataset.

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

Python-based tool designed to process network traffic packets and extract features compliant with the NSL-KDD dataset format.

Hybrid WCGAN-ACGAN framework for balanced network intrusion detection on NSL-KDD and UNSW-NB15 datasets using XGBoost, Decision Trees, CNN, and AutoGluon classifiers

Network Anomaly Detection Using Probabilistic Data Structures