A NOVEL APPROACH FOR NETWORK INTRUSION DETECTION SYSTEM BASED ON MACHINE LEARNING

Yogita Sharma

Abstract


Despite the rapid progress in information technology, the problem of protecting computer and network security remained a major challenge for most researchers, especially after the expansion of networks and evolution of technology and the increasing number of network users and the internet. Networks need some tools for protection, such as firewall, Intrusion Detection Systems (IDSs) and Intrusion Prevention System (IPS). To resolve the problems of IDS this research work proposed “a novel approach for network intrusion detection system based on machine learning”. In our experimental study we use KDDCUP-99 dataset to analyze efficiency of intrusion detection with different WEKA classifiers like Random forest, BayesNet, SMO & J48. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the two algorithms J48 and Random forest gives much better results than other machine learning algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method.

 

Key Words: Network Security, Intrusion Detection, KDD CUP-99 dataset, Machine Learning, Classification


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