Detection of Hardware Trojans in IoT Devices Using mmWave RADAR

The rapid advancement of Internet of Things (IoT) devices has transformed multiple industries. However, the extensive integration of IoT technology has introduced significant security challenges. The physical-layer (PHY) security of IoT devices is vulnerable to malicious hardware modifications, commonly known as hardware Trojans (HTs). Given the unpredictable nature of HTs, there is a need for countermeasures such as radio-frequency fingerprint-based identification methods. This paper presents a novel solution for detecting HTs embedded in wireless devices using millimeter Wave (mmWave) RADAR measurements far outside the operating frequency range of the radar. HT detection using RADAR is demonstrated using a commercial-of-the-shelf mm-wave RADAR, which transmits and receives reflected signals from a passive wireless module designed with a WiFi system-on-chip (ESP8285). The module also includes a custom-made IEEE 802.11 WiFi system-on-chip with signal conditioning components and a hardware Trojan that disrupts the communication link by shorting the antenna. Decision Tree-based model machine-learning approaches are used to successfully identify the presence of HTs from data provided by RADAR measurements, demonstrating their usefulness for large-scale analyses and providing a reliable solution to PHY security challenges. To the best of the authors' knowledge, this is the first time the concept of using RADAR techniques for hardware trojan detection is presented and validated.