Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 10^{-4}$</tex> second inference latency on a laptop CPU.

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Published
2025-01-10
DOI
https://doi.org/10.1109/ccnc54725.2025.10975861
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation

Varun Kohli, Muhammad Naveed Aman, Biplab Sikdar, Bhavya Kohli
Advanced Malware Detection Techniques
article

Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation

Varun Kohli, Muhammad Naveed Aman, Biplab Sikdar, Bhavya Kohli
article en

Abstract

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 10^{-4}$</tex> second inference latency on a laptop CPU.

University of Nebraska–Lincoln (US), Indian Institute of Technology Bombay (IN), National University of Singapore (SG)
Affordable and clean energy
Openalex Percentile: Top 90%
Advanced Malware Detection Techniques
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