Hidden Backdoor Attack against Deep Learning-Based Wireless Signal Modulation Classifiers

Recently, DL has been exploited in wireless communications such as modulation classification. However, due to the openness of wireless channel and unexplainability of DL, it is also vulnerable to adversarial attacks. In this correspondence, we investigate a so called hidden backdoor attack to modulation classification, where the adversary puts elaborately designed poisoned samples on the basis of IQ sequences into training dataset. These poisoned samples are hidden because it could not be found by traditional classification methods. And poisoned samples are same to samples with triggers which are patched samples in feature space. We show that the hidden backdoor attack can reduce the accuracy of modulation classification significantly with patched samples. At last, we propose activation cluster to detect abnormal samples in training dataset.

Sequential Anomaly Detection Against Demodulation Reference Signal Spoofing in 5G NR

In fifth generation (5G) new radio (NR), the demodulation reference signal (DMRS) is employed for channel estimation as part of coherent demodulation of the physical uplink shared channel. However, DMRS spoofing poses a serious threat to 5G NR since inaccurate channel estimation will severely degrade the decoding performance. In this correspondence, we propose to exploit the spatial sparsity structure of the channel to detect the DMRS spoofing, which is motivated by the fact that the spatial sparsity structure of the channel will be significantly impacted if the DMRS spoofing happens. We first extract the spatial sparsity structure of the channel by solving a sparse feature retrieval problem, then propose a sequential sparsity structure anomaly detection method to detect DMRS spoofing. In simulation experiments, we exploit clustered delay line based channel model from 3GPP standards for verifications. Numerical results show that our method outperforms both the subspace dimension based and energy detector based methods.

Physical Layer Security for NOMA-Enabled Multi-Access Edge Computing Wireless Networks

Multi-access edge computing (MEC) has been regarded as a promising technique for enhancing computation capabilities for wireless networks. In this paper, we study physical layer security in an MEC system where multiple users offload partial of their computation tasks to a base station simultaneously based on non-orthogonal multiple access (NOMA), in the presence of a malicious eavesdropper. Secrecy outage probability is adopted to measure the security performance of the computation offloading against eavesdropping attacks. We aim to minimize the sum energy consumption of all the users, subject to constraints in terms of the secrecy offloading rate, the secrecy outage probability, and the decoding order of NOMA. Although the original optimization problem is non-convex and challenging to solve, we put forward an efficient algorithm based on sequential convex approximation and penalty dual decomposition. Numerical results are eventually provided to validate the convergence of the proposed algorithm and its superior energy efficiency with secrecy requirements.