A Security Model for Multi-Agent Based on DFL

Abstract:

Both research and application development in the area of multi-agent currently undertakes rapid expansion but only if adequate security can be provided. Without a correct security mechanism, multi-agent system suffers from fundamental insecurities. By considering both the historical contribution of computer security and the needs of agent-based systems, we present requirements for multi-agent systems security which emphasize the identification and evolution of agents. Existing security models are evaluated against these requirements and generally found wanting. On the other hand, the state of agents have dynamic attribute and that its environment has fuzzy attribute. So, we introduced Dynamic Fuzzy Logic (DFL[14]) to describe agent and the changing of its state. Artificial Immune System (AIS) is a novel evolutionary paradigm inspired by the biological aspects of the immune system. There are widely agreed in characteristics of agents and AIS, such as dynamic, self-study, self-adaptability, self-organization, etc. So it is a natural thing that AI theory is introduced into multi-agent. In this paper, we present the multi-agent immune secure model based on DFL , then a application example is given to simulate the process of the model.

• Periodical: Advanced Materials Research (Volumes 546-547)
• Pages: 1152-1157
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.546-547.1152
• Online since: July 2012
• Authors: Dong Xiao Liu
• Keywords: Agent Immunity, DFL, Immune Algorithm, Multi-Agent System (MAS), Security Patter Recognition

References

[1] J.H. Salter and M.D. Schroeder: the protection of information in computesystems, Proceeding of the IEEE, 1975, 63(9), pp: 1278-1308.

[2] P.A. Crisman, ed., The Compatible Time-SharingSystem. Cambridge, Mass. M.I.T. Press, (1965).

[3] D. Stone: Pdp-10 System concepts andCapabilities, PDP-10 Applications in Science, volII, pp: 32-55.

[4] M. A. Harrisom, W.L. Ruzzo, and J.D. Ullman: protection in operating Systems, Communications of the ACM, 1976, 19(8), pp: 461-471.

DOI: 10.1145/360303.360333

[5] Ben Mankin, Julian Padget,: A Security Model for Multi-agent Systems 1st International Workshop on Safety and Security in Multi-Agent Systems, AAMAS '04, (2004).

[6] Yongsheng Ding: Computation Agent-theory, technology and application(in Chinese), science press, 2004, pp: 325-337.

[7] A Hofmeyr, S Forrest: Architecture of an Artificial Immune System, Evolutionary computation, 2000, 7(1).

[8] Hofmeyr S A, Forrest S: Immunity by Design: An Artificial Immune System, Proc. Of GECCO'99, Orlando, Florida, USA, (1999).

[9] P Ballet, J Tisseau, F Harrouet: A Multi-agent system to model a human humoral response, In the proceeding of the 1997 IEEE International Conference on Systems, Orlando, Florida, (1997).

DOI: 10.1109/icsmc.1997.625776

[10] P Ballet, V Rodin: Immune Mechanisms to Regulate Multi-agents Systems, GECCO 2000, Las Vegas, Nevada, USA, 2000, 7.

[11] Dipankar Dasgupta: An Artificial Immune System as a Multi-Agent Decision Support System, Proceeding of the IEEE International Conference on Systems, Man and Cybernetics(SMC), San Diego, 1998, 8.

DOI: 10.1109/icsmc.1998.726682

[12] N Foukia, D Billard, Pr J Harms: Computer System Immunity using Mobile Agents, 8th HP Open View University Association WS(HPOVUA'2001), Berlin, Germany, 2001, 6.

[13] Srividhya Sathyanath, Ferat Sahin: AISIMAM-An Artificial Immune System Based Intelligent Multi-Agent Model and its Application to a Mine Detection Problem, Proceedings of the ICARIS 2002 1st International Conference on Artificial Immune Systems, Canterbury, UK, September 9 – 11, (2002).

DOI: 10.1109/icsmc.2002.1176015

[14] Fanzhang Li: An Introduction to Dynamic Fuzzy Logic(in Chinese), Yunnan Science Press, 2005. 7.

[15] Xiaoxiao Ma, et, al: the Conception and Model of Immune Agent, (in Chinese) Control and Decision, 2002, 17(4).

[16] Tao Li: Computer Immunology(in Chinese) Publishing House of Electronics Industry, 2005, pp: 173-174.