Exploring the use of fitness landscape analysis for understanding malware evolution

Babaagba, Kehinde; Murali, Ritwik; Thomson, Sarah L.

Abstract

We conduct a preliminary study exploring the potential of using fitness landscape analysis for understanding the evolution of malware. This type of optimisation is fairly new and has not previously been studied through the lens of landscape analysis. We consider Android-based malware evolution and an existing evolutionary algorithm from the literature is used. We construct and visualise search trajectory networks (STNs), which are a new tool aimed at investigation of algorithm behaviour. The STNs indicate that the considered malware spaces may be difficult to navigate under current search operators and that new ones may warrant consideration.

Citation

Babaagba, K., Murali, R., & Thomson, S. L. (2024, July). Exploring the use of fitness landscape analysis for understanding malware evolution. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

• Presentation Conference Type: Conference Abstract
• Conference Name: ACM Genetic and Evolutionary Computation Conference (GECCO) 2024
• Start Date: Jul 14, 2024
• End Date: Jul 18, 2024
• Acceptance Date: May 2, 2024
• Online Publication Date: Aug 1, 2024
• Publication Date: 2024
• Deposit Date: May 3, 2024
• Publicly Available Date: Aug 1, 2024
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Not Peer Reviewed
• Pages: 77-78
• Book Title: Proceedings of the Genetic and Evolutionary Computation Conference Companion
• ISBN: 9798400704956
• DOI: https://doi.org/10.1145/3638530.3664094
• External URL: https://gecco-2024.sigevo.org/