期刊
COMPUTERS & SECURITY
卷 30, 期 6-7, 页码 525-537出版社
ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.cose.2011.06.002
关键词
Autonomic network; Auto defense; Anomaly detection and response; Reputation management; MANET
资金
- Information Security Research Center of NICT, Japan
While anomaly detection and response play a significant role in attaining auto defense, one of core functionalities of autonomic networks, the design and deployment of Anomaly Detection and Response Systems (ADRS) herein is a non-trivial issue because of the special network characteristic, namely self-managing, which requires candidate ADRS to automatically and optimally balance performance objectives and potential negative consequence. In this paper, we propose a decision-theoretic framework to systematically analyze ADRS in autonomic networks, with an objective to achieve its cost-sensitive and self-optimizing operation. In particular, each ADRS agent is viewed as an autonomous entity, making decision as its local operating environment. A global reward signal is then used to quantify the performance of ADRS as a whole in terms of those identified metrics. Furthermore, the analytical framework serves as a basis for developing an adaptive, robust, and near-optimal prototype termed ARSoS, along with a reinforcement learning algorithm for approximately inferring the optimal behavior of a reputation-based ADRS in a specific autonomic network variant, mobile ad-hoc network. The performance of ARSoS is validated through extensive simulations. (C) 2011 Elsevier Ltd. All rights reserved.
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