An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware

Babaagba, Kehinde O.; Wylie, J.M.

doi:10.1145/3583133.3596362

Proceedings of the Companion Conference on Genetic and Evolutionary Computation 2023

DOI: 10.1145/3583133.3596362

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An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware

Kehinde O. Babaagba

J.M. Wylie

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2024

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Exploring the use of fitness landscape analysis for understanding malware evolution

Babaagba,

Murali,

Thomson

2024

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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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.

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Exploring the use of fitness landscape analysis for understanding malware evolution

Babaagba,

Murali,

Thomson

2024

Proceedings of the Genetic and Evolutionary Computation Conference Companion

View full text Add to dashboard Cite

show abstract

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An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware

Cited by 1 publication

References 8 publications

Exploring the use of fitness landscape analysis for understanding malware evolution

Exploring the use of fitness landscape analysis for understanding malware evolution

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