Polymorphic malware detection using sequence classification methods and ensembles

@article{Drew2017PolymorphicMD,
  title={Polymorphic malware detection using sequence classification methods and ensembles},
  author={Jake Drew and Michael Hahsler and Tyler Moore},
  journal={EURASIP J. Information Security},
  year={2017},
  volume={2017},
  pages={2}
}
Identifying malicious software executables is made difficult by the constant adaptations introduced by miscreants in order to evade detection by antivirus software. Such changes are akin to mutations in biological sequences. Recently, high-throughput methods for gene sequence classification have been developed by the bioinformatics and computational biology communities. In this paper, we apply methods designed for gene sequencing to detect malware in a manner robust to attacker adaptations… CONTINUE READING
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International Workshop on Bio-inspired Security, Trust, Assurance and Resilience (BioSTAR 2016). Polymorphic malware detection using sequence classification methods (IEEE

  • J Drew, M Hahsler, T Moore
  • 2016
2 Excerpts

Microsoft Malware Classification Challenge (BIG 2015

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