Intrusion Detection System Using Data Mining Technique | ||||
| Journal of the ACS Advances in Computer Science | ||||
| Article 3, Volume 4, Issue 1, 2010, Page 59-75 PDF (350.69 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asc.2010.158216 | ||||
 View on SCiNiTO | ||||
| Abstract | ||||
| Intrusion detection is an approach for providing a sense of security in existing computer systems and data networks allowing them to operate in their current “open” mode more securely. An intrusion detection system (IDS) inspects all inbound and outbound network activities and identifies suspicious patterns that may indicate a network or system attack from someone attempting to break into or compromise the system. The goal of intrusion detection, then, is to identify, preferably in real time, unauthorized use, misuse, and abuse of computer systems and data networks by both system insides and external penetrators. Nowadays new intelligent techniques have been used to improve the intrusion detection process in computer networks. This paper presents an approach of an adaptive multi-level intrusion detection and prevention system supported with a hybrid intelligent system based on data mining for classification and pattern recognition. We have specified attack signatures, reaction with event communication and correlation that are integrated on the system, incorporating supervised and unsupervised modes, and generating intelligent reasoning. | ||||
| Keywords | ||||
| Intelligence security; Intrusion detection and prevention; Data mining; Classifier | ||||
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