Ross A.J. McLaren
A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs
McLaren, Ross A.J.; Babaagba, Kehinde; Tan, Zhiyuan
Dr Kehinde Babaagba K.Babaagba@napier.ac.uk
Dr Thomas Tan Z.Tan@napier.ac.uk
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.
McLaren, R. A., Babaagba, K., & Tan, Z. (2023). A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs. In Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 19–22, 2022, Revised Selected Papers, Part II (32-46). https://doi.org/10.1007/978-3-031-25891-6_4
|Conference Name||The 8th International Conference on machine Learning, Optimization and Data science - LOD 2022|
|Conference Location||Certosa di Pontignano, Siena – Tuscany, Italy|
|Start Date||Sep 18, 2022|
|End Date||Sep 22, 2022|
|Acceptance Date||Jun 2, 2022|
|Online Publication Date||Mar 10, 2023|
|Deposit Date||Jun 8, 2022|
|Publicly Available Date||Mar 11, 2024|
|Series Title||Lecture Notes in Computer Science|
|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|
|Keywords||Malware, Malware Detection, Adversarial Examples, Generative Adversarial Network (GAN), Behavioural Graphs|
This file is under embargo until Mar 11, 2024 due to copyright reasons.
Contact email@example.com to request a copy for personal use.
You might also like
A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling
A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning
Toward Machine Intelligence that Learns to Fingerprint Polymorphic Worms in IoT