Attack Prevention for Collaborative Spectrum Sensing in Cognitive Radio Networks
Lingjie Duan, Alexander W. Min, Jianwei Huang, and Kang G. Shin

TL;DR
This paper addresses the vulnerability of collaborative spectrum sensing in cognitive radio networks to malicious data falsification attacks and proposes two novel prevention mechanisms that do not require identifying attackers.
Contribution
The paper introduces two attack-prevention mechanisms with direct and indirect punishments that effectively mitigate malicious attacks without needing to identify attackers.
Findings
Direct punishment prevents all attackers from malicious behavior.
Indirect punishment is easier to implement and deters long-term malicious incentives.
Proposed mechanisms improve spectrum sensing robustness against attacks.
Abstract
Collaborative spectrum sensing can significantly improve the detection performance of secondary unlicensed users (SUs). However, the performance of collaborative sensing is vulnerable to sensing data falsification attacks, where malicious SUs (attackers) submit manipulated sensing reports to mislead the fusion center's decision on spectrum occupancy. Moreover, attackers may not follow the fusion center's decision regarding their spectrum access. This paper considers a challenging attack scenario where multiple rational attackers overhear all honest SUs' sensing reports and cooperatively maximize attackers' aggregate spectrum utilization. We show that, without attack-prevention mechanisms, honest SUs are unable to transmit over the licensed spectrum, and they may further be penalized by the primary user for collisions due to attackers' aggressive transmissions. To prevent such attacks,…
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