An enhanced stream mining approach for network anomaly detection

Abdelghani Bellaachia; Rajat Bhatt

doi:10.1117/12.611168

28 March 2005 An enhanced stream mining approach for network anomaly detection

Abdelghani Bellaachia, Rajat Bhatt

Proceedings Volume 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005; (2005) https://doi.org/10.1117/12.611168
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

Network anomaly detection is one of the hot topics in the market today. Currently, researchers are trying to find a way in which machines could automatically learn both normal and anomalous behavior and thus detect anomalies if and when they occur. Most important applications which could spring out of these systems is intrusion detection and spam mail detection. In this paper, the primary focus on the problem and solution of “real time” network intrusion detection although the underlying theory discussed may be used for other applications of anomaly detection (like spam detection or spy-ware detection) too. Since a machine needs a learning process on its own, data mining has been chosen as a preferred technique. The object of this paper is to present a real time clustering system; we call Enhanced Stream Mining (ESM) which could analyze packet information (headers, and data) to determine intrusions.

Citation Download Citation

Abdelghani Bellaachia and Rajat Bhatt "An enhanced stream mining approach for network anomaly detection", Proc. SPIE 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, (28 March 2005); https://doi.org/10.1117/12.611168

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