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InfoResearchPeer-reviewed
Efficient Instruction Vulnerability Prediction With Heterogeneous SDC Propagation Knowledge Graph
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Summary
The paper proposes VP-HPKG, a method that predicts vulnerable instructions to Silent Data Corruption by building a heterogeneous program knowledge graph of instruction and basic-block relations and modeling it with a Graph Neural Network and Transformer. Evaluated with 1,144,070 fault injections using LLFI across 22 programs, it improves accuracy by an average of 10.3% and F1-score by 18.4% over state-of-the-art baselines. It also reduces model complexity by about 2 times and fault injection overhead by 30%.