Generating behavior-based malware detection models with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Tobias Wuchner and Martin Ochoa and Enrico Lovat and 
                 Alexander Pretschner",
  booktitle =    "2016 14th Annual Conference on Privacy, Security and
                 Trust (PST)",
  title =        "Generating behavior-based malware detection models
                 with genetic programming",
  year =         "2016",
  publisher =    "IEEE",
  bibdate =      "2017-05-21",
  bibsource =    "DBLP,
  pages =        "506--511",
  month =        "12-14 " # dec,
  address =      "Auckland, New Zealand",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-4379-8",
  DOI =          "doi:10.1109/PST.2016.7907008",
  abstract =     "Malware remains a major IT security threat and current
                 detection approaches struggle to cope with a
                 professionalized malware development industry. We
                 propose the use of genetic programming to generate
                 effective and robust malware detection models which we
                 call FrankenMods. These are sets of graph metrics that
                 capture characteristic malware behaviour. Evolution of
                 FrankenMods with good detection capabilities yields
                 continuously improved detection effectiveness.
                 FrankenMods are operationalized by evaluating them on
                 quantitative data flow graphs that model malware
                 behaviour as data flows between system resources caused
                 by issued system calls. We show that FrankenMods are
                 substantially more robust and effective than a
                 state-of-the-art graph metric-based detection
  notes =        "Also known as \cite{7907008}",

Genetic Programming entries for Tobias Wuchner Martin Ochoa Enrico Lovat Alexander Pretschner