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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2012.1
- 수록면
- 850 - 864 (15page)
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초록· 키워드
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Due to delay and energy constraints, a cognitive radio may not be able to perform spectrum sensing in all available channels. Therefore, a sensing policy is needed to decide which channels to sense. The channel selection problem is the problem of designing such a sensing policy to maximize throughput while avoiding interference to primary users. The channel selection problem can be formulated as a reinforcement learning problem. Channel selection schemes that employ reinforcement machine learning algorithms are vulnerable to belief manipulation attacks that contaminate the knowledge base of the learning algorithms. In this paper, we analyze the security of channel selection algorithms that are based on reinforcement learning and propose mitigation techniques that make these algorithms more robust against belief manipulation attacks.
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참고문헌 (22)
참고문헌 신청
A. Wald / 1947 / Sequential Analysis / Wiley
B. Bahrak / 2012 / Security of Spectrum Learning in Cognitive Radios
B. Nelson / 2008 / Exploiting machine learning to subvert your spam filter / Proceedings of the First USENIX Workshop on Large- Scale Exploits and Emergent Threats
D. Lowd / 2005 / Adversarial learning / Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 641 ~ 647
E.N. Gilbert / 1960 / Capacity of burst-noise channels / Bell Syst. Tech. J. 39 : 1253 ~ 1265
B. Bahrak / 2012 / Security of Spectrum Learning in Cognitive Radios
B. Nelson / 2008 / Exploiting machine learning to subvert your spam filter / Proceedings of the First USENIX Workshop on Large- Scale Exploits and Emergent Threats
D. Lowd / 2005 / Adversarial learning / Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 641 ~ 647
E.N. Gilbert / 1960 / Capacity of burst-noise channels / Bell Syst. Tech. J. 39 : 1253 ~ 1265
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