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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