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IJMLC 2014 Vol.4(3): 250-255 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.420

Genetic & Evolutionary Feature Selection for Author Identification of HTML Associated with Malware

Henry C. Williams, Joi N. Carter, Willie L. Campbell, Kaushik Roy, and Gerry V. Dozier

Abstract—Malicious software, also known as malware, is a huge problem that costs consumers billions of dollars each year. To solve this problem, a significant amount of research has been dedicated towards detecting malware. In this paper, we introduce a genetic and evolutionary feature selection technique for the identification of HTML code associated with malware. We believe that there may be an association between malware and the HTML code that it is embedded in. Our results show that this technique outperforms previous techniques in terms of recognition accuracy as well as the total number of features needed for recognition.

Index Terms—Authorship classification, biometrics, feature extraction, genetic and evolutionary computation (GEC), malware.

H. C. Williams, J. N. Carter, W. L. Campbell, K. Roy, and G. V. Dozier are with the Computer Science Department, North Carolina Agricultural and Technical State University, Greensboro, NC 27411 USA (e-mail: hcwillia@aggies.ncat.edu, jncarte1@aggies.ncat.edu, wlcampbe@aggies.ncat.edu, kroy@ncat.edu, gvdozier@ncat.edu).

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Cite: Henry C. Williams, Joi N. Carter, Willie L. Campbell, Kaushik Roy, and Gerry V. Dozier, "Genetic & Evolutionary Feature Selection for Author Identification of HTML Associated with Malware," International Journal of Machine Learning and Computing vol.4, no. 3, pp. 250-255, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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