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Over-the-air computation (AirComp) has been shown to provide a significantly higher efficiency than the traditional separating communication and computation tasks in the large-scale wireless sensor network. However, the presence of the malicious active eavesdropper in the network poses a severe security threat, as the eavesdropper can not only intercept aggregated information but also interfere with legitimate transmissions. To address the issue, we first derive the closed-form expressions that characterize both the approximation level and the security level of AirComp in the presence of active eavesdropping. Then, we jointly design the power allocation, the sensor selection, the artificial noise, and the receiver filter to minimize the approximation level subject to the required security level and the power budget constraint. Finally, the penalty dual decomposition scheme is proposed to solve the approximation level minimization problem, which is NP-hard due to the integer-programming nature and coupling properties of the optimization variables. Numerical results show that our scheme significantly outperforms the existing method.

This paper studies the joint secure transceiver design for the full-duplex integrated sensing and communication (ISAC) system, in which the base station performs the target tracking and communicates with the downlink and the uplink users by reusing the resources. Here, the target, referred to as Eve, is a potential eavesdropper with the intention of intercepting both the downlink and the uplink information. The security problem of the full-duplex ISAC system has not been studied well, where the sensing and communication signals suffer from the serious interference. In this paper, we jointly design the information beamformer, the radar waveform, the uplink communication receive filter, and the radar receive filter to achieve the downlink and uplink communication security and the target tracking. Specifically, both the Eve’s signal-to-interference-plus-noise ratio minimization and the secrecy rate maximization problems, subject to the sensing and the communication constraints, are formulated for the ISAC system from the perspectives of the quality of service and the secrecy rate. An iterative algorithm is proposed for solving the formulated problems. We prove that, under appropriate conditions, the proposed iterative algorithm converges to the Karush-Kuhn-Tucker optimal point of the original problem without the rank 1 constraint. We further extend the joint design to the case of the imperfect wiretap channel and the angle uncertainty. Numerical results show that our scheme remarkably outperforms the benchmark approaches. The performance is close to that of designing the secure communication and the sensing separately.

For the modulation classification problems, the deep learning approaches can determine the unknown modulation formats in high confidence. However, it has been maliciously used by eavesdroppers. In this paper, we consider the wireless communication scenario, in which Alice intends to communicate with Bob confidentially in the threat of Eve, who tries to determine the unknown modulation formats of Alice using some deep learning approach. Recent advancements in adversarial machine learning have demonstrated that the deep learning techniques are vulnerable to crafted perturbations. To prevent Eve from classifying Alice’s modulation formats, Alice transmits the modulation signal with the well-designed adversarial perturbation. We first formulate an optimization problem to determine the optimized adversarial perturbation, in which the objective is to mislead the modulation classifier of Eve subject to the communication constraints, i.e., the power efficiency, the achievable rate, and the reliability. Then, the augmented Lagrangian method is adopted to solve the perturbation optimization problem, in which the implicit objective is evaluated using the Monte Carlo method, and the gradients of the implicit constraints are obtained using the Gaussian-based estimation algorithm. We further extend the perturbation design to the both cases of Alice having and not having the prior knowledge of Eve. Finally, the input-independent universal perturbation for the specific modulation type is proposed, which is deployed via a lookup table method. Numerical results show that the designed perturbation with 10% power of the modulated signal can attack Eve’s modulation classifier with the great success while ensuring both the achievable rate and the reliability close to the ideal case (say, no perturbation). Compared to the existing methods, the designed perturbation achieves the better attack performance and is robust to the filtering, the oversampling, and the time/frequency offset. Furthermore, this paper reveals that the structure type of Eve’s model has a large impact on the attack performance, and verifies that the adversarial perturbation can effectively attack the modulation classifiers that resort to the expert knowledge.