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A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs

McLaren, Ross A.J.; Babaagba, Kehinde; Tan, Zhiyuan


Ross A.J. McLaren


As the field of malware detection continues to grow, a shift in focus is occurring from feature vectors and other common, but easily obfuscated elements to a semantics based approach. This is due to the emergence of more complex malware families that use obfuscation techniques to evade detection. Whilst many different methods for developing adversarial examples have been presented against older, non semantics based approaches to malware detection, currently only few seek to generate adversarial examples for the testing of these new semantics based approaches. The model defined in this paper is a step towards such a generator, building on the work of the successful Malware Generative Adversarial Network (MalGAN) to incorporate behavioural graphs in order to build adversarial examples which obfuscate at the semantics level. This work provides initial results showing the viability of the Graph based MalGAN and provides preliminary steps regarding instantiating the model.

Presentation Conference Type Conference Paper (Published)
Conference Name The 8th International Conference on machine Learning, Optimization and Data science - LOD 2022
Start Date Sep 18, 2022
End Date Sep 22, 2022
Acceptance Date Jun 2, 2022
Online Publication Date Mar 10, 2023
Publication Date 2023
Deposit Date Jun 8, 2022
Publicly Available Date Mar 11, 2024
Publisher Springer
Pages 32-46
Series Title Lecture Notes in Computer Science
Series Number 13811
Book Title Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 19–22, 2022, Revised Selected Papers, Part II
ISBN 978-3-031-25890-9
Keywords Malware, Malware Detection, Adversarial Examples, Generative Adversarial Network (GAN), Behavioural Graphs
Public URL


A Generative Adversarial Network Based Approach To Malware Generation Based On Behavioural Graphs (accepted version) (316 Kb)

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