Machine-Learning-Based Attestation for the Internet of Things Using Memory Traces

Abstract

The advent of 4G and 5G mobile networks has made the Internet of Things (IoT) devices an essential part of smart nation drives. Firmware integrity is crucial to the security of IoT systems. Most of the existing techniques for firmware attestation require a legitimate copy of an IoT device’s firmware. However, firmware is considered an intellectual property (IP) of the manufacturer and may not be available. To solve this issue, this article proposes a software-based attestation technique where remote verifiers use machine learning (ML) classifiers on an IoT device’s memory dump to verify the integrity of an IoT device’s internal state. The experimental results from an actual prototype show that the proposed technique not only successfully detects attacks with high accuracy but also results in about 96% lower latency as compared to existing techniques. All this is achieved with high availability, low computational complexity, and without requiring a legitimate copy of the device’s original firmware.