Hassan Aboushady

Abdelrahman Emad Abdelazim, Alan Rodrigo Diaz-Rizo, Hassan Aboushady et al.

Covert channels (CCs) in wireless chips pose a serious security threat, as they enable the exfiltration of sensitive information from the chip to an external attacker. In this work, we propose an AI-based defense mechanism deployed at the RF receiver, where the model directly monitors raw I/Q samples to detect, in real time, the presence of a CC embedded within an otherwise nominal signal. We first compact a state-of-the-art convolutional neural network (CNN), achieving an 80% reduction in parameters, which is an essential requirement for efficient edge deployment. When evaluated on the open-source hardware Trojan (HT)-based CC dataset, the compacted CNN attains an average accuracy of 90.28% for CC detection and 86.50% for identifying the underlying HT, with results averaged across SNR values above 1 dB. For practical communication scenarios where SNR > 20 dB, the model achieves over 97% accuracy for both tasks. These results correspond to a minimal performance degradation of less than 2% compared to the baseline model. The compacted CNN is further benchmarked against alternative classifiers, demonstrating an excellent accuracy-model size trade-off. Finally, we design a lightweight CNN hardware accelerator and demonstrate it on an FPGA, achieving very low resource utilization and an efficiency of 107 GOPs/W. Being the first AI hardware accelerator proposed specifically for CC detection, we compare it against state-of-the-art AI accelerators for RF signal classification tasks such as modulation recognition, showing superior performance.

Valentin Barbaza, Alan Rodrigo Diaz-Rizo, Hassan Aboushady et al.

AI models are often regarded as valuable intellectual property due to the high cost of their development, the competitive advantage they provide, and the proprietary techniques involved in their creation. As a result, AI model stealing attacks pose a serious concern for AI model providers. In this work, we present a novel attack targeting wireless devices equipped with AI hardware accelerators. The attack unfolds in two phases. In the first phase, the victim's device is compromised with a hardware Trojan (HT) designed to covertly leak model weights through a hidden communication channel, without the victim realizing it. In the second phase, the adversary uses a nearby wireless device to intercept the victim's transmission frames during normal operation and incrementally reconstruct the complete weight matrix. The proposed attack is agnostic to both the AI model architecture and the hardware accelerator used. We validate our approach through a hardware-based demonstration involving four diverse AI models of varying types and sizes. We detail the design of the HT and the covert channel, highlighting their stealthy nature. Additionally, we analyze the impact of bit error rates on the reception and propose an error mitigation technique. The effectiveness of the attack is evaluated based on the accuracy of the reconstructed models with stolen weights and the time required to extract them. Finally, we explore potential defense mechanisms.