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When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing
Defense strategies have been well studied to combat Byzantine attacks that
aim to disrupt cooperative spectrum sensing by sending falsified versions of
spectrum sensing data to a fusion center. However, existing studies usually
assume network or attackers as passive entities, e.g., assuming the prior
knowledge of attacks is known or fixed. In practice, attackers can actively
adopt arbitrary behaviors and avoid pre-assumed patterns or assumptions used by
defense strategies. In this paper, we revisit this security vulnerability as an
adversarial machine learning problem and propose a novel learning-empowered
attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion
center. Based on the black-box nature of the fusion center in cooperative
spectrum sensing, our new perspective is to make the adversarial use of machine
learning to construct a surrogate model of the fusion center's decision model.
We propose a generic algorithm to create malicious sensing data using this
surrogate model. Our real-world experiments show that the LEB attack is
effective to beat a wide range of existing defense strategies with an up to 82%
of success ratio. Given the gap between the proposed LEB attack and existing
defenses, we introduce a non-invasive method named as influence-limiting
defense, which can coexist with existing defenses to defend against LEB attack
or other similar attacks. We show that this defense is highly effective and
reduces the overall disruption ratio of LEB attack by up to 80%