AMMF: cross-architectures vulnerability detection based on attention mechanism and multi-feature fusion

Abstract Binary vulnerability detection plays an important role in the field of program security. In order to deal with large-scale vulnerability detection tasks, more and more neural network technologies are applied to cross-architectures vulnerability detection. These technologies have significantly improved the accuracy of vulnerability detection. However, existing methods still face problems such as single extracted information, poor robustness against compilation optimization, and inability to perform cross-architectures vulnerability detection. Therefore, this paper proposes a cross-architectures vulnerability detection method based on attention mechanism and multi-feature fusion. This method can simultaneously obtain information such as assembly code, attribute control flow graph and function-level features for cross-architecture, cross-compilers and cross-optimization options vulnerability detection. Adding attention mechanism to GRU and GoogleNet improves the model to obtain semantic information and attribute information after the fusion of basic block-level and function-level features, and searches for Top-K suspected vulnerability functions and graph matching through deep neural network model to perform phased vulnerability detection. The experimental results show that this method achieves an accuracy of 95.79% and a Recall of 97.06%, which is better than the existing methods and performs well in vulnerability detection in real environments.