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Risk prediction of malicious code-infected websites by mining vulnerability features

Authors
Lee, T.Kim, D.Jeong, H.In, H.P.
Issue Date
2014
Keywords
Classification; Feature modeling; Vulnerability identification
Citation
International Journal of Security and its Applications, v.8, no.1, pp.291 - 294
Indexed
SCOPUS
Journal Title
International Journal of Security and its Applications
Volume
8
Number
1
Start Page
291
End Page
294
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/100866
DOI
10.14257/ijsia.2014.8.1.27
ISSN
1738-9976
Abstract
Malicious-code scanning tools are practically available for identifying suspicious websites. However, such tools only warn users about suspicious sites and do not provide clues as to why the sites were hacked and which vulnerability was responsible for the attack. In addition, the huge number of alarms burdens mangers while executing in-time-response duties. In this paper, a process involving feature modeling and data-mining techniques is proposed to help solve such problems. © 2014 SERSC.
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