This is the strong baseline in final competition (KDD1999) of NTU Machine Learning 2016 Fall lectured by Hung-Yi Lee
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Updated
Jan 18, 2017 - Python
This is the strong baseline in final competition (KDD1999) of NTU Machine Learning 2016 Fall lectured by Hung-Yi Lee
Solutions to kdd99 dataset with Decision tree and Neural network by scikit-learn
Project developed during Network Security class at Federal University of Rio de Janeiro on spring 2017
Feature coded UNSW_NB15 intrusion detection data.
Assess various ML algorithms on KDD99 network dataset then apply the best algorithm (Random Forest) using R.
ISSS610 Applied Machine Learning
This repository contains a notebook implementing an autoencoder based approach for intrusion detection, the full documentation of the study will be available shortly.
Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
An introductory course to pandas and scikit learn
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
Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn
Machine Learning with the NSL-KDD dataset for Network Intrusion Detection
Creating an Intrusion Detection System
Anomaly detection using machine learning
Intrusion detection using machine learning for KDD 99 dataset
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
Cyber-attack classification in the network traffic database using NSL-KDD dataset
A Tensorflow model to detect network intrusions in the KDD Cup 1999 data-set.
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