基于深度学习的软件漏洞检测方法与传统检测模式的对比分析

Comparative Analysis of Software Vulnerability Detection Method Based on Deep Learning and Traditional Detection Mode

软件安全是软件开发与应用领域的关键问题,漏洞检查在软件发展的任何阶段都是十分重要且颇具难度的任务。较为完善的漏洞检测技术,除了能准确地识别出潜藏的恶意代码外,还能收集到更多的实时数据,以便开发人员及时采取措施,防止可能发生的潜在损失。现有的软件漏洞检测技术各有所长,且随着深度学习技术的发展而发展,利用其来检测软件漏洞,可以发现和修复更多的问题,并精确地指出缺陷的种类。为有效解决漏洞的多分类问题,文中提出了新的解决方案。

Software security is a key issue in software development and application field,and bug review is a very important and difficult task at any stage of software development.A relatively complete vulnerability detection technology can not only accurately identify the hidden malicious code,but also collect more real-time data,so that developers can take timely measures to prevent possible potential losses.Existing software vulnerability detection technologies have their own strengths,and with the development of deep learning technology,using it to detect software vulnerabilities can find and fix more problems,and accurately point out the types of defects.In order to effectively solve the problem of multi-classification of vulnerabilities,this paper proposes a new solution.