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ICSE2022顶会

A Grounded Theory Based Approach to Characterize Software Attack Surfaces

Sara Moshtari, Ahmet Okutan, Mehdi Mirakhorli

2022年份
7被引次数
1顶会引用

摘要

The notion of Attack Surface refers to the critical points on the boundary of a software system which are accessible from outside or contain valuable content for attackers. The ability to identify attack surface components of software system has a significant role in effectiveness of vulnerability analysis approaches. Most prior works focus on vulnerability techniques that use an approximation of attack surfaces and there have not been many attempts to create a comprehensive list of attack surface components. Although limited number of studies have focused on attack surface analysis, they defined attack surface components based on project specific hypotheses to evaluate security risk of specific types of software applications. In this study, we leverage a qualitative analysis approach to empirically identify an extensive list of attack surface components. To this end, we conduct a Grounded Theory (GT) analysis on 1444 previously published vulnerability reports and weaknesses with a team of three software developers and security experts. We extract vulnerability information from two publicly available repositories: 1) Common Vulnerabilities and Exposures (CVE) and 2) Common Weakness Enumeration (CWE). We ask three key questions: where the attacks come from, what they target, and how they emerge, and to help answer these questions we define three core categories for attack surface components: Entry points, Targets, and Mechanisms. We extract attack surface concepts related to each category from collected vulnerability information using the GT analysis and provide a comprehensive categorization that represents attack surface components of software systems from various perspectives. The paper introduces 254 new attack surface components that did not exist in the literature. The comparison of the proposed attack surface model with prior works indicates that only 6.7% of the identified Code level attack surface components are studied before.

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