Skip to main navigation Skip to search Skip to main content

Generating behavior-based malware detection models with genetic programming

  • Technical University of Munich
  • Singapore University of Technology and Design

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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 behavior. 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 behavior 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 approach.

Original languageEnglish
Title of host publication2016 14th Annual Conference on Privacy, Security and Trust, PST 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages506-511
Number of pages6
ISBN (Electronic)9781509043798
DOIs
StatePublished - 2016
Event14th Annual Conference on Privacy, Security and Trust, PST 2016 - Auckland, New Zealand
Duration: 12 Dec 2016 → 14 Dec 2016

Publication series

Name2016 14th Annual Conference on Privacy, Security and Trust, PST 2016

Conference

Conference14th Annual Conference on Privacy, Security and Trust, PST 2016
Country/TerritoryNew Zealand
CityAuckland
Period12/12/16 → 14/12/16

Fingerprint

Dive into the research topics of 'Generating behavior-based malware detection models with genetic programming'. Together they form a unique fingerprint.

Cite this