Beschreibung
Software vulnerabilities can lead to monetary and information losses. Moreover, detecting vulnerability is very resource intensive. Due to limited human and financial resources, prioritization of vulnerabilities is a crucial task. Very little attention has been paid to textual information hidden in vulnerabilities databases like CVE and OSVDB. This research explores how to use from the information hidden in these texts to vulnerability prediction and clustering.
Autorenportrait
She is a PhD candidate in Software Engineering at the Yazd University, Yazd, Iran. She has obtained her B.S. in Software Engineering from Emam Reza University, Mashhad, Iran and her M.S in Software Engineering from Shiraz University, Shiraz, Iran. Her research interests include Security and Natural Language Processing.
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