Abstract
An intrusion detection system (IDS) monitors network traffic and monitors for suspicious activity and alerts the system or network administrator. It identifies unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. IDS's are based on the belief that an intruder's behavior will be noticeably different from that of a legitimate user. Many IDS has been designed and implemented using various techniques like Data Mining, Fuzzy Logic, Neural Network etc. This paper investigates the problem of existing normal Data Mining Techniques which is not efficient enough for the IDS performance. In this paper we have proposed a Stream Data Mining and Drift Detection Method which is more suitable for Machine learning technique to model efficient Intrusion Detection Systems.
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