Prediction of instruction SDC vulnerability in routing algorithms based on graph convolutional network

Zhijun Liu; Yi Zhuang

doi:10.1117/12.3052148

15 January 2025 Prediction of instruction SDC vulnerability in routing algorithms based on graph convolutional network

Zhijun Liu, Yi Zhuang

Author Affiliations +

Proceedings Volume 13516, Fourth International Conference on Network Communication and Information Security (ICNCIS 2024); 135160X (2025) https://doi.org/10.1117/12.3052148
Event: International Conference on Network Communication and Information Security (ICNCIS 2024), 2024, Hangzhou, China

Abstract

The transient faults caused by high-energy particles in the space radiation environment impact the reliability of routing algorithms in network communication mechanisms, where SDC (Silent Data Corruption) poses a significant threat to space-based network systems due to its insidious nature. Given that existing SDC fault tolerance methods do not incorporate the characteristics of space-based network communication mechanisms and require time-consuming fault injections, this paper proposes a method for strengthening the SDC vulnerability of network routing algorithms based on GCN (Graph Convolutional Networks). First, the impact of transient faults on routing algorithms is analyzed, and a fault model of the routing algorithm is constructed. Next, the instruction features and instruction dependencies of the routing algorithm are extracted to construct an instruction dependency graph. Then, a GCN-based instruction SDC vulnerability prediction model is constructed and trained to predict the SDC vulnerability of the routing algorithm instructions. Finally, the routing algorithm is reinforced to enable self-detection and fault tolerance of SDC. Experimental results show that, compared to existing methods, the proposed method achieves higher accuracy in SDC vulnerability prediction and detection rates without requiring large-scale fault injection.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhijun Liu and Yi Zhuang "Prediction of instruction SDC vulnerability in routing algorithms based on graph convolutional network", Proc. SPIE 13516, Fourth International Conference on Network Communication and Information Security (ICNCIS 2024), 135160X (15 January 2025); https://doi.org/10.1117/12.3052148

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KEYWORDS

Feature extraction

Computing systems

Matrices

Machine learning

Polonium

Telecommunications

Tolerancing

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