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

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

An Anomaly based Intrusion Detection System: A Robust Machine Learning Approach

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

A Tensorflow model to detect network intrusions in the KDD Cup 1999 data-set.

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

[Anomaly detection] refers to the process of identifying patterns in data that do not conform to expected behavior. This project aims to develop a machine learning model to predict and identify potential attacks in IoT networks, thus helping to secure these networks from malicious activities.