Towards Cross Project Vulnerability Detection in Open Source Mobile Applications

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dc.contributor.authorObiria, P. B.
dc.date.accessioned2024-03-01T13:43:25Z
dc.date.available2021-01-22T09:46:03Z
dc.date.available2024-03-01T13:43:25Z
dc.date.issued2020-12
dc.descriptionJournal articleen_US
dc.description.abstractOpen source software avails its design publicly posing enormous risk to enterprises. Early vulnerabilities detection is an important step towards building secure open source mobile applications. Cross project vulnerability detection plays significant roles in appraising the most likely vulnerable components. However, current vulnerability detection models that identify vulnerable components are focused on reducing time and effort needed to secure software using process or product metrics and machine learning techniques. Conversely, little effort has been spent to deliver modalities to choose the training data for cross project vulnerability detection. Therefore, we present an empirical study and experiment to demystify cross project open source mobile app vulnerability detection using clustering, a data mining technique to group similar data. The study will be conducted on publicly available dataset of several open source mobile applications and in the context of cross project vulnerability detection.
dc.description.sponsorshipPAC University
dc.identifier.citationObiria, P. (2020). Towards Cross Project Vulnerability Detection in Open Source Mobile Applications. PAC University Journal of Arts and Social Sciences, 2(2), 1-12. Retrieved from http://js.delaintechnologies.com/index.php/PACUJASS/article/view/3
dc.identifier.urihttps://repo.pacuniversity.ac.ke/handle/123456789/3070.2
dc.language.isoenen_US
dc.subjectMobile applicationsen_US
dc.subjectOpen source systemsen_US
dc.subjectvulnerability detectionen_US
dc.titleTowards Cross Project Vulnerability Detection in Open Source Mobile Applicationsen_US
dc.typeArticleen_US
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