IoT Device Authentication via RAM Trace Analysis: A Representation Learning Framework

Recent advances in IoT, machine learning, and edge computing have driven transformative paradigms like smart cities, grids, healthcare, and transportation systems, providing efficient solutions. This has led to a pervasive proliferation of connected devices, ranging from high-power computers to low-power sensors. Yet, the complex IoT architecture poses numerous vulnerabilities, demanding robust security measures. Existing firmware attestation techniques often encounter obstacles due to proprietary constraints, necessitating access to the device’s authentic firmware. To address this challenge, this paper proposes a novel software-based attestation framework that utilizes RAM traces from IoT devices for remote verification. By employing deep learning models trained in a representation learning paradigm, our framework empowers the remote verifier to authenticate the internal state of IoT devices. Leveraging data collected from real-world prototype devices, our approach achieves an impressive 100% detection rate for critical attacks on IoT devices with a false positive rate of 10−3. Remarkably, our framework preserves device availability and maintains low authentication latency, highlighting its efficacy and practicality for securing IoT ecosystems.