Rizwan Hamid Randhawa
Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection
Randhawa, Rizwan Hamid; Aslam, Nauman; Alauthman, Mohammad; Khalid, Muhammad; Rafiq, Husnain
Authors
Nauman Aslam
Mohammad Alauthman
Dr Muhammad Khalid M.Khalid@hull.ac.uk
Lecturer/Assistant Professor
Husnain Rafiq
Abstract
Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at recognising adversarial evasions. However, the synthetic evasions may not follow the original semantics of the input samples. This paper proposes a novel GAN model leveraged with deep reinforcement learning (DRL) to explore semantic aware samples and simultaneously harden its detection. A DRL agent is used to attack the discriminator of the GAN that acts as a botnet detector. The agent trains the discriminator on the crafted perturbations during the GAN training, which helps the GAN generator converge earlier than the case without DRL. We name this model RELEVAGAN, i.e. [“relieve a GAN” or deep REinforcement Learning-based Evasion Generative Adversarial Network] because, with the help of DRL, it minimises the GAN's job by letting its generator explore the evasion samples within the semantic limits. During the GAN training, the attacks are conducted to adjust the discriminator weights for learning crafted perturbations by the agent. RELEVAGAN does not require adversarial training for the ML classifiers since it can act as an adversarial semantic-aware botnet detection model. The code will be available at https://github.com/rhr407/RELEVAGAN.
Citation
Randhawa, R. H., Aslam, N., Alauthman, M., Khalid, M., & Rafiq, H. (2024). Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection. Future generations computer systems : FGCS, 150, 294-302. https://doi.org/10.1016/j.future.2023.09.011
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 3, 2023 |
Online Publication Date | Sep 7, 2023 |
Publication Date | Jan 1, 2024 |
Deposit Date | Nov 2, 2023 |
Publicly Available Date | Nov 3, 2023 |
Journal | Future Generation Computer Systems |
Print ISSN | 0167-739X |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 150 |
Pages | 294-302 |
DOI | https://doi.org/10.1016/j.future.2023.09.011 |
Keywords | Low data regimes; GANs; ACGAN; EVAGAN; Botnet |
Public URL | https://hull-repository.worktribe.com/output/4430435 |
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Publisher Licence URL
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Copyright Statement
©2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
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