Metamodel for reputation based agents system: case study for electrical distribution SCADA design

ABSTRACT

SCADA systems are urged to face more and more complex critical situation and thereby, need to continuously evolve towards integrated decision making capability driven by fancy reaction strategy. The current research stream in that field, aims, accordingly, to foster the smartness of the field equipment and actuators, which predominately exist under the concept of agents. Those agents are governed by policies that while dictating the agent behavior, depending on the agent roles and the context of evolution, also confer to the latter the latitude to react based on their own perception of the evolving environment. This agent ability is referring to as the agent smartness and is strongly determined by, and depending on, the trust perceived by the agent of its environment. Actual work related to agents tends to consider that agents evolve and are organized in systems. There exist some models for representing how these agents are organized at a high level, models for representing how they are spread in the networks, models to represent how they communicate to each other, and so forth. However, as far as we know, no model exists that integrates all of the above models. Therefore, we do believe that such an integrated model could have many advantages like e.g. to know the impact from the action from one layer to another, to decide which action on a component has the most important impact on a set of other components, to identify the most critical component for an infrastructure, to align the agent system with the corporate objective and to tailor it accordingly. Therefore, we have decided to frame an innovative version of ArchiMate® for the multi-agent purpose with the objective to enrich the agent society collaborations and, more particularly, the description of the agent behavior endorsed in the policy component and using a reputation based trust model to improve the reliability, termed ARMAN. Our work has been illustrated in the frame of a critical infrastructure in the field of electrical power distribution which is a highly sensitive research topic.

References

1. M. Lankhorst. ArchiMate language primer, 2004.
2. J. A. Zachman. 2003. The Zachman Framework For Enterprise Architecture: Primer for Enterprise Engineering and Manufacturing By. Engineering, no. July: 1--11.
3. F. Zambonelli, N. R. Jennings, and M. Wooldridge. 2003. Developing multiagent systems: The Gaia methodology. ACM Trans. Softw. Eng. Methodol. 12, 3 (July 2003), 317--370.
4. V. Torres da Silva, R. Choren, and C. J. P. de Lucena. 2004. A UML Based Approach for Modeling and Implementing Multi-Agent Systems. AAMAS '04, Vol. 2. IEEE, Washington, USA, 914--921.
5. C. Feltus, E. Dubois, E. Proper, I. Band, Michaël Petit, Enhancing the ArchiMate® Standard with a Responsibility Modeling Language for Access Rights Management, 5th ACM International Conference on Security of Information and Networks (ACM SIN 2012), Jaipur, Rajastan, India. doi<10.1145/2388576.2388577
6. G. Guemkam, C. Feltus, C. Bonhomme, P. Schmitt, B. Gâteau, D. Khadraoui, Z. Guessoum, Financial Critical Infrastructure: A MAS Trusted Architecture for Alert Detection and Authenticated Transactions, Sixth IEEE Conference on Network Architecture and Information System Security (IEEE SAR/SSI2011), France. doi<10.1109/SAR-SSI.2011.5931359
7. G. Guemkam, D. Khadraoui, B. Gâteau, Z. Guessoum, ARMAN: Agent-based Reputation for Mobile Ad hoc Networks. 11th Conference on Practical Applications of Agents and Multi-Agent Systems. Salamanca, Spain 22nd-24 May 2013. Doi< 10.1007/978-3-642-38073-0_11
8. J. Blangenois, G. Guemkam, C. Feltus, D. Khadraoui, Organizational Security Architecture for Critical Infrastructure, 8th International Workshop on Frontiers in Availability, 8^th FARES 2013, IEEE, Germany.

Index Terms

• Information systems

  • Data management systems

    • Database administration