Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Resource Allocation for Wireless Networks: Basics, Techniques, and Applications
Resource Allocation for Wireless Networks: Basics, Techniques, and Applications
Cognitive Spectrum and Its Security Issues
NGMAST '08 Proceedings of the 2008 The Second International Conference on Next Generation Mobile Applications, Services, and Technologies
Dynamic Spectrum Access and Management in Cognitive Radio Networks
Dynamic Spectrum Access and Management in Cognitive Radio Networks
Dogfight in spectrum: jamming and anti-jamming in multichannel cognitive radio systems
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
CatchIt: detect malicious nodes in collaborative spectrum sensing
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks
IEEE Transactions on Wireless Communications
Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks
IEEE Transactions on Wireless Communications
Defense against Primary User Emulation Attacks in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications
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Collaborative spectrum sensing is subject to the attack of malicious secondary user(s), which may send false reports. Therefore, it is necessary to detect potential attacker(s) and then exclude the attacker's report for spectrum sensing. Many existing attacker-detection schemes are based on the knowledge of the attacker's strategy and thus apply the Bayesian attacker detection. However, in practical cognitive radio systems the data fusion center typically does not know the attacker's strategy. To alleviate the problem of the unknown strategy of attacker(s), an abnormality-detection approach, based on the abnormality detection in data mining, is proposed. The performance of the attacker detection in the single-attacker scenario is analyzed explicitly. For the case in which the attacker does not know the reports of honest secondary users (called independent attack), it is shown that the attacker can always be detected as the number of spectrum sensing rounds tends to infinity. For the case in which the attacker knows all the reports of other secondary users, based on which the attacker sends its report (called dependent attack), an approach for the attacker to perfectly avoid being detected is found, provided that the attacker has perfect information about the miss-detection and false-alarm probabilities. This motivates cognitive radio networks to protect the reports of secondary users. The performance of attacker detection in the general case of multiple attackers is demonstrated using numerical simulations.