Science and Development Network (SciDev.Net)
The cyber-attacks represent one of the most dangerous secret weapons. Intrusion detection system is an important tool to protect the authors' systems and networks against the various forms of attacks. The purpose of this paper is to build a fast and high performance hybrid hierarchical intrusion detection system called NFPHIDS that possesses the following characteristics: have a short training time, detect the low frequent attacks, give a high detection rate for frequent attacks, and give a low false alarm rate. NFPHIDS contains two levels. The first one includes four fast classifiers Random Forest, Simple Cart, Best first decision tree, Naive Bayes used for their excellent performance on the detection of respectively Normal behavior and DOS, Probe, R2L, and U2R.