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Real-time identification of anomalous packet payloads for network intrusion detection

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3 Author(s)
Nwanze, N. ; Dept. of Electr. & Comput. Eng., State Univ. of New York, Binghamton, NY, USA ; Summerville, D.H. ; Skormin, V.A.

A preliminary evaluation of a real-time packet-level anomaly detection approach for network intrusion detection in high-bandwidth network environments is presented. The approach characterizes network traffic using a novel technique that maps packet-level payloads onto a set of counters using bit-pattern hash functions. Machine learning is accomplished by mapping unlabelled training data onto a set of two-dimensional grids and forming a set of bitmaps that identify anomalous and normal regions. These bitmaps are used as the classifiers for real-time detection. Preliminary results using the DARPA intrusion detection evaluation data sets yield a 100% detection of all applicable attacks, with very low false positive rate. Furthermore, the approach is able to detect nearly all of the individual packets that comprised each attack.

Published in:

Information Assurance Workshop, 2005. IAW '05. Proceedings from the Sixth Annual IEEE SMC

Date of Conference:

15-17 June 2005

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