Research on Software Vulnerability Detection Methods Based on Deep Learning

Abstract

This paper aims to investigate software vulnerability detection methods based on deep learning to address the ever-growing challenges in software security. With the rapid development of information technology, software vulnerabilities have become the primary targets of cyberattacks, posing severe threats to economic, military, and social security. By analyzing the limitations of existing software vulnerability detection methods, this paper explores the potential applications of deep learning technology in this field. Through a literature review, relevant theories of software vulnerabilities are introduced, including the concept, types, and impacts of vulnerabilities. Subsequently, detailed descriptions of deep learning-based vulnerability detection methods are provided, encompassing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and their variant LSTM, as well as the application of program slicing techniques in vulnerability detection. By evaluating and improving existing deep learning models, a novel deep learning-based vulnerability detection framework is proposed, with its workflow and technical principles elaborated. The proposed deep learning-based vulnerability detection method can effectively enhance the accuracy and efficiency of software vulnerability detection, reduce reliance on expert knowledge, and lower human resource costs. Experimental results demonstrate that this method performs exceptionally well on multiple datasets, indicating broad application prospects and significant research value. The deep learning-based software vulnerability detection method offers new insights and tools for addressing current software security challenges. In the future, with the continuous development and improvement of deep learning technology, this method will play an even more crucial role in the field of software vulnerability detection.