利用分析代码图表示的算法组合搜索软件漏洞

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS
G. S. Kubrin, D. P. Zegzhda
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引用次数: 0

摘要

摘要 本文分析了现有的软件漏洞搜索方法。对于在代码的图表示上使用深度学习模型的方法,提出了程序之间的想象关系问题,这使其在代码分析问题上的应用变得复杂。为了解决这个问题,我们提出了一种迭代方法,该方法基于分析代码图表示的算法集合。该方法依赖于逐步缩小所考虑的代码部分的范围,以提高使用计算复杂度高的方法的效率。针对所提出的方法,介绍了基于 .NET 平台的程序漏洞搜索系统原型,并在 NIST SARD 和具有大量代码的软件样本上进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Searching for Software Vulnerabilities Using an Ensemble of Algorithms for the Analysis of a Graph Representation of the Code

Searching for Software Vulnerabilities Using an Ensemble of Algorithms for the Analysis of a Graph Representation of the Code

Searching for Software Vulnerabilities Using an Ensemble of Algorithms for the Analysis of a Graph Representation of the Code

This article analyzes the existing methods for searching for software vulnerabilities. For methods using deep learning models on a graph representation of the code, the problem of imaginary relationships between procedures is formulated, which complicates their application to code analysis problems. To solve the formulated problem, an iterative method is proposed based on an ensemble of algorithms for analyzing the graph representation of the code. The method relies on a step-by-step narrowing of the set of code sections under consideration to increase the efficiency of using highly computationally complex methods. For the proposed method, a prototype of a system for searching for vulnerabilities for programs based on the .NET platform is presented, tested on a sample of NIST SARD and software with a large amount of code.

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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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