Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
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Updated
May 8, 2019 - Python
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 repository contains a notebook implementing an autoencoder based approach for intrusion detection, the full documentation of the study will be available shortly.
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.
This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets
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
IDS based on Machine Learning technical
Scripts for downloading, preprocessing, and numpy-ifying popular machine learning datasets
A comparison between Statistical, Machine Learning, PCA, SVD, and REF methods
A network Intrusion Detection System (IDS) based on Self-Organizing Neural Networks (SOINN).
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.
Cyber Security: Development of Network Intrusion Detection System (NIDS), with Machine Learning and Deep Learning, Recurrent Neural Network models, MERN web I/O System.
An in-depth comparison of two prominent intrusion detection datasets: KDDCup99 and NSL-KDD
The following program is capable of analyzing network traffic with multiple different machine learning (ML) and feature selection algorithms to determine whether or not it is malicious.
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