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Anomaly Detection System Using Resource Pattern Learning

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4 Author(s)
Ohno, Y. ; Dept. of Comput. Sci. & Eng., Waseda Univ., Tokyo, Japan ; Sugaya, M. ; van der Zee, A. ; Nakajima, T.

In this paper, Anomaly Detection by Resource Monitoring (Ayaka), a novel lightweight anomaly and fault detection infrastructure, is presented for Information Appliances. Ayaka provides a general monitoring method for detecting anomalies using only resource usage information on systems independent of its domain, target application and programming languages. Ayaka modifies the kernel to detect faults and uses a completely application black-box approach based on machine learning methods. It uses the clustering method to quantize the resource usage vector data and learn the normal patterns with Hidden Markov Model. In the running phase, Ayaka finds anomalies by comparing the application resource usage with learned model. The evaluation experiment indicates that our prototype system is able to detect anomalies, such as SQL injection and buffer overrun, without significant overheads.

Published in:

Future Dependable Distributed Systems, 2009 Software Technologies for

Date of Conference:

17-17 March 2009

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