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Highlighting Vulnerabilities via Context-Aware Framework

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dc.contributor.author Chang, Eric
dc.date.accessioned 2017-02-01T19:33:24Z
dc.date.available 2017-02-01T19:33:24Z
dc.date.issued 2017 en_US
dc.identifier.uri https://hdl.handle.net/10535/10260
dc.description.abstract It is known to be full of challenges to score vulnerabilities based on various security requirements in cloud services. Although there have been several systems for scoring vulnerabilities (e.g., CVSS), most of them are unable to be leveraged to score vulnerabilities in cloud services, because they fail to consider some important factors located in cloud such as business context (i.e., dependency relationships between services) and threat levels of the same vulnerability on various security requirements. This paper aims to propose a novel framework to qualify and rank the vulnerabilities based on their threat degrees in cloud service. Through inputting or constructing service dependency graph, our framework is able to generate the importance degree of each service and the ranking list of all the vulnerabilities in cloud service. Moreover, our framework can be adopted not only into various cloud infrastructures, but also different categories of algorithms according to concrete requirements. To evaluate our framework, we adopt AssetRank algorithm into the framework, and present the whole design of our work. Comprehensive experiments prove the effectiveness of our framework on qualifying and ranking vulnerabilities in cloud service. en_US
dc.language English en_US
dc.relation.ispartofseries Working Paper en_US
dc.subject models en_US
dc.subject.classification Computer Science en_US
dc.title Highlighting Vulnerabilities via Context-Aware Framework en_US
dc.type Working Paper en_US
dc.type.methodology Case Study en_US
dc.publisher.workingpaperseries Yale University en_US
dc.coverage.region North America en_US
dc.coverage.country USA en_US
dc.subject.sector Information & Knowledge en_US

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