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

Taek Lee, Dohoon Kim, Hyunchoel Jeong, Hoh In

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)291-294
Number of pages4
JournalInternational Journal of Security and its Applications
Volume8
Issue number1
DOIs
Publication statusPublished - 2014 Feb 20

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Keywords

  • Classification
  • Feature modeling
  • Vulnerability identification

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Risk prediction of malicious code-infected websites by mining vulnerability features. / Lee, Taek; Kim, Dohoon; Jeong, Hyunchoel; In, Hoh.

In: International Journal of Security and its Applications, Vol. 8, No. 1, 20.02.2014, p. 291-294.

Research output: Contribution to journalArticle

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