Zhao Dongmei
College of Information Technology, Hebei Normal University, Shijiazhuang 050024, China
Liu Jinxing
Department of Airborne Weapon, The First Aeronautics College of PLAAF, XinYang 464000, China
Zhang Yanxue
College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024, China
ABSTRACT
Under the complex, dynamic and uncertain networks threat environment, the network defense decision must be made effectively so that the limited network defense resource, such as firewall, honeypot, reacting resource, etc., can be distributed effectively, the primary information asset can be protected and the mainly network part can be operated effectively. The mental attributions are the foundation of making network defense decisions. In this study, we try to model the mental attribution of the defense decision by means of visualization method, to establish a foundation for developing the network defense decision system. First, the mental factors of the automatics decision are classified from information, motivation and operation of the decision based on the requirements of networks security defense; and then, based on the traditional BDI-Agent (Belief, Desire and Intention), a BGP-Agent model is presented, which has the three types of mental attributions, i.e. the belief, goal and defense plan. Finally, the mental attributions of the network defense decision and the relationship among the mental attributions are modeled by the Agent diagram and the mental diagram with the Agent Modeling Language (AML).
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How to cite this article
Zhao Dongmei, Liu Jinxing and Zhang Yanxue, 2013. Modeling Mental Attributions of Defense Decision of Networks Security Based on AML. Information Technology Journal, 12: 8123-8128.
DOI: 10.3923/itj.2013.8123.8128
URL: https://scialert.net/abstract/?doi=itj.2013.8123.8128
DOI: 10.3923/itj.2013.8123.8128
URL: https://scialert.net/abstract/?doi=itj.2013.8123.8128
REFERENCES
- Boukerche, A., R.B. Machado, K.R.L. Juca, J.B.M. Sobral and M.S.M.A. Notare, 2007. An agent based and biological inspired real-time intrusion detection and security model for computer network operations. Comput. Commun., 30: 2649-2660.
CrossRefDirect Link - Hausken, K. and G. Levitin, 2009. Minmax defense strategy for complex multi-state systems. Reliab. Eng. Syst. Saf., 94: 577-587.
CrossRefDirect Link - Trencansky, I. and R. Cervenka, 2005. Agent Modeling Language (AML): A comprehensive approach to modeling MAS. Informatica, 29: 391-400.
Direct Link - Zaki, M. and T.S. Sobh, 2004. A cooperative agent-based model for active security systems. J. Network Comput. Appl., 27: 201-220.
CrossRefDirect Link