- author = "Kevin Leach and Ryan Dougherty and Chad Spensky and Stephanie Forrest and Westley Weimer",
- title = "Evolutionary Computation for Improving Malware Analysis",
- booktitle = "GI-2019, ICSE workshops proceedings",
- year = "2019",
- editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer",
- pages = "18--19",
- address = "Montreal",
- month = "28 " # may,
- publisher = "IEEE",
- note = "Best Presentation",
- keywords = "genetic algorithms, genetic programming, genetic improvement",
- isbn13 = "978-1-7281-2268-7",
-
URL = "
http://dijkstra.eecs.umich.edu/kleach/malware-gi-19.pdf",
-
URL = "http://geneticimprovementofsoftware.com/paper_pdfs/leach2019evolutionary.pdf",
-
DOI = "
DOI:10.1109/GI.2019.00013",
- size = "2 pages",
- abstract = "Research in genetic improvement (GI) conventionally focuses on the improvement of software, including the automated repair of bugs and vulnerabilities as well as the refinement of software to increase performance. Eliminating or reducing vulnerabilities using GI has improved the security of benign software, but the growing volume and complexity of malicious software necessitates better analysis techniques that may benefit from a GI-based approach. Rather than focus on the use of GI to improve individual software artefacts, we believe GI can be applied to the tools used to analyse malicious code for its behaviour. First, malware analysis is critical to understanding the damage caused by an attacker, which GI-based bug repair does not currently address. Second, modern malware samples leverage complex vectors for infection that cannot currently be addressed by GI. In this paper, we discuss an application of genetic improvement to the realm of automated malware analysis through the use of variable-strength covering arrays.",
-
notes = "stealthy malware samples
Slides: http://geneticimprovementofsoftware.com/slides/leach2019evolutionary_slides.pdf
GI-2019 http://geneticimprovementofsoftware.com
part of \cite{Petke:2019:ICSEworkshop}",
