Anti-worm Immunization of Web System Based on Normal Model and BP Neural Network
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.
KeywordsRecognition Rate Normal Model Adaptive Learning Artificial Immune System Worm Propagation
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