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General Information
ISSN:
1796-2021 (Online); 2374-4367 (Print)
Abbreviated Title:
J. Commun.
Frequency:
Monthly
DOI:
10.12720/jcm
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Scopus
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E-mail questions
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Acceptance Rate:
27%
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800 USD
Average Days to Accept:
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3.4
2023
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Editor-in-Chief
Prof. Maode Ma
College of Engineering, Qatar University, Doha, Qatar
I'm very happy and honored to take on the position of editor-in-chief of JCM, which is a high-quality journal with potential and I'll try my every effort to bring JCM to a next level...
[Read More]
What's New
2024-08-20
Vol. 19, No. 8 has been published online!
2024-07-22
Vol. 19, No. 7 has been published online!
2024-06-20
Volume 19, No. 4 has been indexed by Scopus.
Home
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Published Issues
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2015
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Volume 10, No. 2, February 2015
>
ASVC: An Automatic Security Vulnerability Categorization Framework Based on Novel Features of Vulnerability Data
Tao Wen
1
, Yuqing Zhang
1,2
, Qianru Wu
2
, and Gang Yang
2
1.State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
2.National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, Beijing 101408, China
Abstract
— Security vulnerabilities are a main cause of network security. Vulnerability classification gives us a better understanding of the essence of vulnerabilities, which help propose efficient solutions. However, applying Vulnerability Categorization Standard (VCS) to manually categorize vulnerabilities is impracticable since it is time-consuming and subjective. To address this issue, a new framework named Automatic Security Vulnerabilities Categorization Framework (ASVC) is proposed based on Text Mining. To further improve the accuracy, a new rule for extraction of features of Text Mining is proposed. ASVC abstracts the categorization of vulnerabilities into a process of Text Mining, and categorize vulnerabilities automatically according to a VCS. Finally, VCS of Common Weakness Enumeration is applied to a main Vulnerability Database based on ASVC in a fast way, about 1000 vulnerabilities per hour. The accuracy of the categorization is 82.5%, 4.5% higher than previous works.
Index Terms
—Security vulnerability, vulnerability categorization, vulnerability database, information security, asvc, text mining
Cite: Tao Wen, Yuqing Zhang, Qianru Wu, and Gang Yang, "ASVC: An Automatic Security Vulnerability Categorization Framework Based on Novel Features of Vulnerability Data," Journal of Communications, vol. 10, no. 2, pp. 107-116, 2015. Doi: 10.12720/jcm.10.2.107-116
20160128020950974
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