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

Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

Feature coded UNSW_NB15 intrusion detection data.

Solutions to kdd99 dataset with Decision tree and Neural network by scikit-learn

修改谷歌提供的样例量子卷积神经网络模型，基于KDD99数据集进行训练，实现了网络攻击分类检测。

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

using machine-learning to detecte instruction

Abnormal Traffic Identification Classifier based on Machine Learning. My code for undergraduate graduation design.

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

Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn

Kdd99 dataset analyzing and some data reproducing experiments with SDN

COSC 490 Towson University

Assess various ML algorithms on KDD99 network dataset then apply the best algorithm (Random Forest) using R.

An Intrusion Detection System (IDS) implemented in Python, which utilizes machine learning techniques and the KDD Cup 1999 dataset to detect and classify network intrusions in real-time.

ISSS610 Applied Machine Learning

This is the strong baseline in final competition (KDD1999) of NTU Machine Learning 2016 Fall lectured by Hung-Yi Lee

Demo of SciKit ML algorithms using the kdd99 dataset

Work with NSL-KDD to reach best accuracy