Are evolutionary rule learning algorithms appropriate for malware detection?

Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Volumn , Issue , 2009, Pages 1915-1916

  Shafiq, M Zubair ^a   Tabish, S Momina ^a   Farooq, Muddassar ^a  

^a NATIONAL UNIVERSITY OF COMPUTER AND EMERGING SCIENCES   (Pakistan)

Author keywords

Genetics based machine learning; Malware detection

Indexed keywords

CLASSIFICATION ACCURACY; COMPARATIVE STUDIES; DATA SETS; EVOLUTIONARY RULES; EXECUTABLES; GENETICS BASED MACHINE LEARNING; MALICIOUS EXECUTABLES; MALWARE DETECTION; MICROSOFT WINDOWS; PROCESSING OVERHEAD; REAL-WORLD; RULE LEARNING; RULE LEARNING ALGORITHMS; RULE SET;

CHROMOSOMES; COMPUTER VIRUSES; EDUCATION; EVOLUTIONARY ALGORITHMS; ROBOT LEARNING; WINDOWS OPERATING SYSTEM;

LEARNING ALGORITHMS;

EID: 72749091289     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1570233     Document Type: Conference Paper

References (4)

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2. J. Alcala-Fdez, L. Sanchez, S. Garcia, M.J. del Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernandez, F. Herrera, "KEEL: a software tool to assess evolutionary algorithms for data mining problems", Soft Computing, Volume 13, pp. 307-318, Springer, 2009.
3. Microsoft Portable Executable and Common Object File Format Specification, Windows Hardware Developer Central, Updated March 2008.
4. VX Heavens Virus Collection, VX Heavens website, available at http://vx.netlux.org.