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Character-Level Convolutional Neural Network for Predicting Severity of Software Vulnerability from Vulnerability Description
Shunta NAKAGAWA Tatsuya NAGAI Hideaki KANEHARA Keisuke FURUMOTO Makoto TAKITA Yoshiaki SHIRAISHI Takeshi TAKAHASHI Masami MOHRI Yasuhiro TAKANO Masakatu MORII
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/09/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section LETTER (Special Section on Log Data Usage Technology and Office Information Systems)
CVE, CVSS, Convolutional Neural Network,
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System administrators and security officials of an organization need to deal with vulnerable IT assets, especially those with severe vulnerabilities, to minimize the risk of these vulnerabilities being exploited. The Common Vulnerability Scoring System (CVSS) can be used as a means to calculate the severity score of vulnerabilities, but it currently requires human operators to choose input values. A word-level Convolutional Neural Network (CNN) has been proposed to estimate the input parameters of CVSS and derive the severity score of vulnerability notes, but its accuracy needs to be improved further. In this paper, we propose a character-level CNN for estimating the severity scores. Experiments show that the proposed scheme outperforms conventional one in terms of accuracy and how errors occur.