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LiteAtt: Secure and Seamless IoT Services Using TinyML-based Self-Attestation as a Primitive

arXiv Security Archived Mar 23, 2026 ✓ Full text saved

arXiv:2603.19727v1 Announce Type: new Abstract: 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 ver

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    Computer Science > Cryptography and Security [Submitted on 20 Mar 2026] LiteAtt: Secure and Seamless IoT Services Using TinyML-based Self-Attestation as a Primitive Varun Kohli, Biplab Sikdar 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. Comments: Submitted to IEEE TDSC Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.19727 [cs.CR]   (or arXiv:2603.19727v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.19727 Focus to learn more Submission history From: Varun Kohli [view email] [v1] Fri, 20 Mar 2026 08:10:14 UTC (2,132 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Mar 23, 2026
    Archived
    Mar 23, 2026
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