Binary software vulnerability detection method based on attention mechanism

Aiming at the stack overflow vulnerability in binary software, this paper proposes a binary vulnerability detection method based on the attention mechanism. First, this paper analyze the basic characteristics of stack overflow vulnerabilities, and perform data preprocessing on the decompiled files to make the neural network better adapt to the characteristics of stack overflow vulnerabilities, then formulate instruction specifications at the assembly language level, and finally input the data into the fusion attention mechanism Learning in the neural network. This paper compares and analyzes three kinds of neural networks on the CWE121 data set. The experimental results show that after neural network training, the detection method based on the attention mechanism can be effective and accurately discover whether the target area has stack overflow vulnerabilities, thereby greatly improving the detection efficiency.