The worm program model: an application centred point of view for distributed architecture design
EW 3 Proceedings of the 3rd workshop on ACM SIGOPS European workshop: Autonomy or interdependence in distributed systems?
Code red worm propagation modeling and analysis
Proceedings of the 9th ACM conference on Computer and communications security
How to Own the Internet in Your Spare Time
Proceedings of the 11th USENIX Security Symposium
Worm Propagation and Generic Attacks
IEEE Security and Privacy
Immunizing mobile ad hoc networks against collaborative attacks using cooperative immune model
Security and Communication Networks
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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.