A Survey on Software Vulnerability Exploitability Assessment

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Sarah Elder, Rayhanur Rahman, Gage Fringer, Kunal Kapoor, Laurie Williams
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引用次数: 0

Abstract

Knowing the exploitability and severity of software vulnerabilities helps practitioners prioritize vulnerability mitigation efforts. Researchers have proposed and evaluated many different exploitability assessment methods. The goal of this research is to assist practitioners and researchers in understanding existing methods for assessing vulnerability exploitability through a survey of exploitability assessment literature. We identify three exploitability assessment approaches: assessments based on original, manual CVSS, automated Deterministic assessments, and automated Probabilistic assessments. Other than the original CVSS, the two most common subcategories are Deterministic, Program-State-Based, and Probabilistic Learning Model (LM) Assessments.

软件漏洞可利用性评估调查
了解软件漏洞的可利用性和严重性有助于从业人员确定漏洞缓解工作的优先次序。研究人员提出并评估了许多不同的可利用性评估方法。本研究的目的是通过对漏洞可利用性评估文献的调查,帮助从业人员和研究人员了解现有的漏洞可利用性评估方法。我们确定了三种可利用性评估方法:基于原始手工 CVSS 的评估、自动确定性评估和自动概率评估。除原始 CVSS 外,最常见的两个子类别是确定性评估、基于程序状态的评估和概率学习模型 (LM) 评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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