Key points are not available for this paper at this time.
The cyber-attacks represent one of the most dangerous secret weapons. Intrusion detection system is an important tool to protect our 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. Only five outputs of the first level are selected, and used as inputs of the second level that contains Naive Bayes as final classifier. The experimentation on KDD99 shows the high performance of our model compared to the results obtained by some wellknown classifiers.
Ahmim et al. (Mon,) studied this question.