RAM-Based Firmware Attestation for IoT Security: A Representation Learning Framework
Asif Iqbal, Usman Zia, Muhammad Naveed Aman, Biplab Sikdar · IEEE Internet of Things Journal · 2024
With the proliferation of 4G and 5G mobile networks in smart cities, the adoption of Internet of Things (IoT) devices has surged, emphasizing the critical need for robust security measures. Existing firmware attestation techniques often require high computational budget or access to the device’s authentic firmware, posing challenges due to resource and proprietary constraints. To counter these two fundamental challenges, this article introduces a novel software-based attestation framework utilizing RAM traces from IoT devices for remote verification. In the proposed framework, the need for an authentic firmware copy is eliminated, and the most computationally intensive task is assigned to the gateway node of the IoT ecosystem. This approach yields a robust and highly accurate device attestation strategy, while imposing minimal computational demands on the verification device itself. Employing deep learning models trained in a representation learning paradigm, our framework enables the remote verifier to authenticate the internal state of IoT devices. Leveraging data collected from real-world prototype devices, under eight different applications, our approach achieves a remarkable 100% accuracy in detecting critical attacks on IoT devices with a false positive rate of$10^{-3}$. Notably, our framework preserves device availability and maintains low authentication latency, underscoring its efficacy and practicality for securing IoT ecosystems.