基于复合式神经网络的二进制软件漏洞检测方法

Binary software vulnerability detection method based on composite neural network

摘要:

在软件漏洞检测领域,传统神经网络模型和图神经网络模型是已被验证的有效方法。目前,方案大多针对源代码进行漏洞检测,运用神经网络模型对二进制软件进行漏洞检测的研究相对较少,更是缺乏对图神经网络在二进制软件漏洞检测方面的研究。为充分研究神经网络模型在二进制软件漏洞检测方面的有效性,提出了一种基于复合式神经网络的二进制软件漏洞检测方法。将二进制代码向量化表示为同时支持传统神经网络模型和图神经网络模型训练的图数据结构;使用传统神经网络模型和图神经网络模型相结合的复合式神经网络模型对图数据结构进行学习和验证;在公开的二进制软件漏洞数据集上进行实验和对比分析,结果表明该方法能够有效提升漏洞检测能力,在准确率、精确度等性能指标方面都有明显提升。

Abstract:

Both traditional neural network models and graph neural network models have been validated as effective methods in the field of software vulnerability detection, but most of the current solutions are aimed at source code for vulnerability detection, and relatively little research has been conducted on applying neural network models directly to binary software for vulnerability detection, especially graph neural networks for binary software vulnerability detection is lacking. In order to fully investigate the effectiveness of neural network models in binary software vulnerability detection, this paper proposed a composite neural network based binary software vulnerability detection method. Firstly, the binary code was vectorised into a graph data structure that supports both traditional neural network models and graph neural network models for training. Then, a composite neural network model that combines traditional neural network models and graph neural network models was used to learn and validate the graph data structure. Finally, experiments and comparative analysis were conducted on a publicly available binary software vulnerability dataset. The experimental results show that the method could effectively improve vulnerability detection capabilities, with significant improvements in performance metrics such as accuracy and precision.