As the Internet of Things (IoT) becomes an integral part of critical infrastructure, smart cities, and consumer networks, there has been an increase in the number of software attacks on the microcontrollers (MCUs) that constitute such networks. Runtime firmware attestation, i.e., the verification of a firmware's integrity, has become instrumental, and prior work focuses on lightweight IoT MCUs, offloading the verification task to capable remote verifiers. However, modern IoT devices feature large flash and volatile memory, on-device TinyML inference, and Trusted Execution Environments (TEE). Leveraging these capabilities, this paper presents a verifier-less, hybrid Self-Attestation (SA) framework called LiteAtt, which is based on TinyML execution in the Arm TrustZone of an IoT MCU for quick, on-device evaluation of the IoT firmware's SRAM footprint. LiteAtt takes a step towards ubiquitous intelligence and decentralized trust in IoT networks. It eliminates the need for firmware copies for attestation, and protects the privacy of user SRAM data by leveraging twin devices to train the TinyML models. The proposed framework achieves an average accuracy of 98.7%, F1 score of 99.33%, TPR of 98.72%, and TNR of 97.45% on SRAM attestation datasets collected from real devices. LiteAtt operates with a latency of 1.29ms, an energy consumption of 42.79uJ, and a runtime memory overhead of up to 32KB, which is suitable for battery-operated Arm Cortex-M devices. A security analysis is provided for the protocol regarding mutual authentication, confidentiality, integrity, SRAM privacy, and defense against replay and impersonation attacks. Practical deployment scenarios and future works are also discussed.

翻译：随着物联网（IoT）成为关键基础设施、智慧城市和消费网络的重要组成部分，针对构成此类网络的微控制器（MCU）的软件攻击日益增多。运行时固件验证（即固件完整性校验）已成为关键手段，已有研究主要关注轻量级IoT MCU，并将验证任务卸载至能力更强的远程验证方。然而，现代IoT设备配备大容量闪存和易失性内存、支持设备端TinyML推理及可信执行环境（TEE）。本文利用上述能力，提出一种基于Arm TrustZone IoT MCU中TinyML执行的无验证方混合自证框架LiteAtt。该框架通过在设备端快速评估IoT固件SRAM内存占用，向实现IoT网络中的泛在智能与去中心化信任迈进一步。LiteAtt消除了固件副本用于验证的需求，并通过孪生设备训练TinyML模型保护用户SRAM数据隐私。在真实设备采集的SRAM验证数据集上，本框架达到平均准确率98.7%、F1分数99.33%、真正例率98.72%、真负例率97.45%的性能。LiteAtt运行时延迟为1.29ms，能耗42.79uJ，运行时内存开销最高32KB，适用于电池供电的Arm Cortex-M设备。本文还从协议层面分析了双向认证、机密性、完整性、SRAM隐私保护及抗重放与冒充攻击能力，并讨论了实际部署场景与未来工作。