Abstract
Pattern recognition and learning of unknown worms have become a bottleneck of network security since a lot of variants of old worms and new worms occurred. To overcome this bottleneck, many traditional approaches were tested but failed. In this paper, a normal model of a web system was proposed to detect all selfs and all non-selfs, especially all unknown worms. The normal model was built on the 2-dimension attributes of space and time of the system. Moreover, a BP neural network was used to design an adaptive learning mechanism of the immunized web system. The non-self learning was utilized to recognize most unknown worms through the trained BP network, which was trained with the feature data in the worm database. Besides, the innate non-self selection was designed to recognize all known worms. Experiments validated effectiveness of this approach on the BP network and the normal model.
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© 2006 Springer-Verlag Berlin Heidelberg
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Gong, T., Cai, Z. (2006). Anti-worm Immunization of Web System Based on Normal Model and BP Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_39
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DOI: https://doi.org/10.1007/11760191_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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