AI-Enabled Covert Channel Detection in RF Receiver Architectures
This work addresses the security threat of covert channels in wireless chips for hardware security practitioners, but the approach is incremental as it compresses an existing CNN and applies it to a known dataset.
The paper proposes an AI-based defense mechanism for detecting covert channels in RF receivers by monitoring raw I/Q samples. The compacted CNN achieves 90.28% accuracy for CC detection and 86.50% for identifying the underlying hardware Trojan, with over 97% accuracy at SNR > 20 dB, while reducing parameters by 80% and enabling efficient FPGA deployment at 107 GOPs/W.
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.