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Attacking Spectrum Sensing With Adversarial Deep Learning in Cognitive Radio-Enabled Internet of Things

Liu, Mingqian and Zhang, Hongyi and Liu, Zilong and Zhao, Nan (2022) 'Attacking Spectrum Sensing With Adversarial Deep Learning in Cognitive Radio-Enabled Internet of Things.' IEEE Transactions on Reliability. pp. 1-14. ISSN 0018-9529

Attacking_Spectrum_Sensing_With_Adversarial_Deep_Learning_in_Cognitive_Radio-Enabled_Internet_of_Things.pdf - Accepted Version

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Cognitive radio-based Internet of Things (CR-IoT) network provides a solution for IoT devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in CR-IoT network, which has been investigated extensively based on deep learning (DL). Despite the unique advantages of DL in spectrum sensing, the black-box and unexplained properties of deep neural networks may lead to many security risks. This article considers the fusion of traditional interference methods and data poisoning which is an attack method on the training data of a machine learning tool. We propose a new adversarial attack for reducing the sensing accuracy in DL-based spectrum sensing systems. We introduce a novel design of jamming waveform whose interference capability is reinforced by data poisoning. Simulation results show that significant performance enhancement and higher mobility can be achieved compared with traditional white-box attack methods.

Item Type: Article
Uncontrolled Keywords: Adversarial attack; data poisoning; internet-of-things (IoTs); spectrum sensing; waveform design
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 12 Sep 2022 12:46
Last Modified: 23 Sep 2022 19:54

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