Abstract
Under the normalized network security situation of artificial intelligence-assisted attacks, deep semantic obfuscation has become the core means of vulnerability hiding. Attackers evade detection by legitimizing semantic associations and obfuscating code logic, posing serious threats to infrastructure and software supply chain security. Therefore, to enhance the robustness and accuracy of vulnerability detection under deep semantic obfuscation scenarios, the research proposes a vulnerability detection method based on tensor circulant matrix. The method preserves code semantic integrity and local correlations based on tensor circulant matrix. On this basis, it combines Gated Graph Convolutional Neural Networks (GGCNN) to improve the model’s feature capture capability for hidden vulnerabilities. On obfuscated vulnerability datasets, the average detection accuracy for obfuscated vulnerabilities reaches 96.24%, precision reaches 83.62%, recall reaches 87.53%, and F1 score reaches 85.54%. Compared with the Long Short-Term Memory Network (LSTM) baseline model, the False Negative Rate (FNR) has decreased by 16.73%, demonstrating significantly improved robustness under semantic obfuscation scenarios. The vulnerability detection model constructed in this study can effectively resist semantic obfuscation interference, provides a reliable technical approach for deep semantic obfuscation vulnerability detection, and has important practical significance for strengthening code security protection.
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