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Deep Reinforcement Adversarial Learning Against Botnet Evasion Attacks
Open accessAbstract
As cybersecurity detectors increasingly rely on machine learning mechanisms, attacks to these defenses escalate as well. Supervised classifiers are prone to adversarial evasion, and existing countermeasures suffer from many limitations. Most solutions degrade performance in the absence of adversarial perturbations; they are unable to face novel attack variants; they are applicable only to specific machine learning algorithms. We propose the first framework that can protect botnet detectors from adversarial attacks through deep reinforcement learning mechanisms. It automatically generates realistic attack samples that can evade detection, and it uses these samples to produce an augmented training set for producing hardened detectors. In such a way, we obtain more resilient detectors that can work even against unforeseen evasion attacks with the great merit of not penalizing their performance in the absence of specific attacks. We validate our proposal through an extensive experimental campaign that considers multiple machine learning algorithms and public datasets. The results highlight the improvements of the proposed solution over the state-of-the-art. Our method paves the way to novel and more robust cybersecurity detectors based on machine learning applied to network traffic analytics.
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“This process enables the building of diffusion coefficient-based images depending on different sources and classifications.”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [73]
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“Moreover, GAN is used to create raw image datasets by multiple hospitals and then share those raw imprints rather than actual images for privacy preservation.”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [73]
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“It has been observed using experimental analysis that the algorithm provides 97% accuracy on cancer datasets and outperforms other non-FL-based schemes.”
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Zatika, A., & Coffman, J. (2024) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc63386.2024.00037 · Cited by 1
Verkerken, M., Callewaert, M., D’hooge, L., Wauters, T., Volckaert, B., & Turck, F. D. (2025) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw67616.2025.00030 · Cited by 1
Electricity Theft Detection Scheme Based on CNN-AE-LSTM Hybrid Model Against Evasion Attacks
Xia, Z., Lin, X., Zhou, K., & Tan, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227935 · Cited by 1
Robustness Evaluation of Network Intrusion Detection Systems based on Sequential Machine Learning
Venturi, A., Zanasi, C., Marchetti, M., & Colajanni, M. (2022) · IEEE International Symposium on Network Computing and Applications (NCA) · doi:10.1109/nca57778.2022.10013643 · Cited by 3
Self-citationUniversal Adversarial Attacks for Visual Odometry Systems
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Research on Robustness Enhancement Method of Deep Reinforcement Learning Uav Path Planning AlgorithmPreprint
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Metadata
- Authors
- Apruzzese, G., Andreolini, M., Marchetti, M., Venturi, A., & Colajanni, M.
- Affiliations
- University of Liechtenstein (LI)
- University of Modena and Reggio Emilia (IT)
- University of Bologna (IT)
- Venue
- IEEE Transactions on Network and Service Management
- Published
- 2020-12
- Volume
- 17
- Issue
- 4
- Pages
- 1975-1987
- ISSN
- 1932-4537 (ISSN-L)
- 2373-7379
- OpenAlex
- W3111563271
- Cited by
- 117
- References
- 77
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 28 · 2023: 21 · 2022: 23 · 2021: 12 · 2020: 1
- FWCI
- 5.60
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Transactions on Network and Service Management (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- Opin vísindi (Opin vísindi) (submittedVersion, cc-by-nc-sa)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- botnet detection, evasion attacks, deep reinforcement learning, adversarial attack generation, data augmentation, network traffic analysis
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