Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Keywords = relational graph neural networks (RGNNs)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
18 pages, 1261 KB  
Article
Firmware Attestation in IoT Swarms Using Relational Graph Neural Networks and Static Random Access Memory
by Abdelkabir Rouagubi, Chaymae El Youssofi and Khalid Chougdali
AI 2025, 6(7), 161; https://doi.org/10.3390/ai6070161 - 21 Jul 2025
Viewed by 1099
Abstract
The proliferation of Internet of Things (IoT) swarms—comprising billions of low-end interconnected embedded devices—has transformed industrial automation, smart homes, and agriculture. However, these swarms are highly susceptible to firmware anomalies that can propagate across nodes, posing serious security threats. To address this, we [...] Read more.
The proliferation of Internet of Things (IoT) swarms—comprising billions of low-end interconnected embedded devices—has transformed industrial automation, smart homes, and agriculture. However, these swarms are highly susceptible to firmware anomalies that can propagate across nodes, posing serious security threats. To address this, we propose a novel Remote Attestation (RA) framework for real-time firmware verification, leveraging Relational Graph Neural Networks (RGNNs) to model the graph-like structure of IoT swarms and capture complex inter-node dependencies. Unlike conventional Graph Neural Networks (GNNs), RGNNs incorporate edge types (e.g., Prompt, Sensor Data, Processed Signal), enabling finer-grained detection of propagation dynamics. The proposed method uses runtime Static Random Access Memory (SRAM) data to detect malicious firmware and its effects without requiring access to firmware binaries. Experimental results demonstrate that the framework achieves 99.94% accuracy and a 99.85% anomaly detection rate in a 4-node swarm (Swarm-1), and 100.00% accuracy with complete anomaly detection in a 6-node swarm (Swarm-2). Moreover, the method proves resilient against noise, dropped responses, and trace replay attacks, offering a robust and scalable solution for securing IoT swarms. Full article
Show Figures

Figure 1

Back to TopTop