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

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

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

Feature coded UNSW_NB15 intrusion detection data.

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

using machine-learning to detecte instruction

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

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

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

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

COSC 490 Towson University

ISSS610 Applied Machine Learning

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

This is a classification model with five classes (normal, DOS, R2L, U2R,PROBING). Ignore the content features of TCP connection ( columns 10-22 of KDD Cup 99 dataset) when training the model to adapt the project that a kdd99 feature extractor

Demo of SciKit ML algorithms using the kdd99 dataset

Intrusion detection using machine learning for KDD 99 dataset

Project developed during Network Security class at Federal University of Rio de Janeiro on spring 2017

An introductory course to pandas and scikit learn

Creating an Intrusion Detection System