Covert Communication Channels Based On Hardware Trojans: Open-Source Dataset and AI-Based Detection

The threat of Hardware Trojan-based Covert Channels (HT-CCs) presents a significant challenge to the security of wireless communications. In this work, we generate in hardware and make open-source a dataset for various HT-CC scenarios. The dataset represents transmissions from a HT-infected RF transceiver hiding a CC that leaks information. It encompasses a wide range of signal impairments, noise levels, and HT insertions, facilitating a robust evaluation of HT-CC attack models and defenses. We also propose a deep learning-based HT-CC detection defense that achieves excellent accuracy on the dataset. It is an one fit all solution that circumvents the cost of integrating several distinct defenses to deal with all known HT-CC scenarios.