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A Framework for Extrusion Detection Using Machine Learning

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2 Author(s)
Yan Luo ; Dept. of Comput. Sci., Illinois Univ., Chicago, IL ; Tsai, J.J.P.

Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. They are particularly useful for (a) poorly understood problem domains where little knowledge exists for the humans to develop effective algorithms; (b) domains where there are large databases containing valuable implicit regularities to be discovered; or (c) domains where programs must adapt to changing conditions. Not surprisingly, the field of Cyber space turns out to be a fertile ground where many software security problems could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning in extraction detection. In the paper, we present our research work on design and implementation of an extrusion detection system for information security of big companies. The result shows a potential in real-world applications.

Published in:

Object Oriented Real-Time Distributed Computing (ISORC), 2008 11th IEEE International Symposium on

Date of Conference:

5-7 May 2008

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