A Non-Intrusive and Context-Based Vulnerability Scoring Framework for Cloud Services
Hao Zhuang, Florian Pydde

TL;DR
This paper introduces NCVS, a practical framework that automatically assesses vulnerability severity in cloud services by considering contextual information, providing more relevant scores than traditional methods like CVSS.
Contribution
The paper presents a novel, non-intrusive, context-aware vulnerability scoring framework for cloud services that improves relevance and accuracy of severity assessments.
Findings
NCVS produces more relevant vulnerability scores than CVSS.
It automatically collects contextual information without source code modifications.
NCVS effectively models service context using a dependency graph.
Abstract
Understanding the severity of vulnerabilities within cloud services is particularly important for today service administrators.Although many systems, e.g., CVSS, have been built to evaluate and score the severity of vulnerabilities for administrators, the scoring schemes employed by these systems fail to take into account the contextual information of specific services having these vulnerabilities, such as what roles they play in a particular service. Such a deficiency makes resulting scores unhelpful. This paper presents a practical framework, NCVS, that offers automatic and contextual scoring mechanism to evaluate the severity of vulnerabilities for a particular service. Specifically, for a given service S, NCVS first automatically collects S contextual information including topology, configurations, vulnerabilities and their dependencies. Then, NCVS uses the collected information to…
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Taxonomy
TopicsSoftware System Performance and Reliability · Network Security and Intrusion Detection · Information and Cyber Security
