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Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE (2008)
Dec. 19, 2008 to Dec. 20, 2008
ISBN: 978-0-7695-3490-9
pp: 482-487
Newly emerged applications are producing a large amount of traffic and connection in the Internets. And they are becoming increasingly difficult to detect. Signature based method are currently the approaches for discovering and detecting the patterns of application. However, these methods may confront their difficulty in validating the efficiency and quality of signatures for unknown applications. Therefore, how to generate the more accurate and representative patterns and validate the quality of signatures is a critical issue.In this paper, a new method has been proposed with a new structure to generate high quality signatures. Different from traditional methods, this one employs a signature learning mechanism that is designed to refine the signatures by merging the similar patterns to improve the signature quality. The experiment indicates that this method is efficient to generate accurate and robust signatures. And the quality of signatures is improved by signature learning.
signature generation, clustering, string alignment
Ming-wei Zhang, Dai-ping Liu, "Scalable and Accurate Application Signature Discovery", Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE, vol. 01, no. , pp. 482-487, 2008, doi:10.1109/PACIIA.2008.104
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