The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
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.”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [73]
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
The Limitations of Deep Learning in Adversarial Settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016) · 2016 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2016.36 · Cited by 3,993
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
One Pixel Attack for Fooling Deep Neural Networks
Su, J., Vargas, D. V., & Sakurai, K. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2890858 · Cited by 2,406
Chen, P., Zhang, H., Sharma, Y., Yi, J., & Hsieh, C. (2017) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3128572.3140448 · Cited by 1,864
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
Du, M., Li, F., Zheng, G., & Srikumar, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134015 · Cited by 1,835
Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B. A., Srndic, N., Laskov, P., Giacinto, G., & Roli, F. (2013) · Cited by 1,658
Wild patterns: Ten years after the rise of adversarial machine learning
Biggio, B., & Roli, F. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.023 · Cited by 1,563
Research works printed but not linked: 2
- anderson, 2017, Evading machine learning malware detection
- 2020, Checkpoint 2020 Security Report
Cited by
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Items 101–117 of 117
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Samtani, S., Raff, E., & Anderson, H. S. (2024) · Digital Threats Research and Practice · doi:10.1145/3652029
Encrypted Malicious Traffic Detection Based on Albert and Deep LearningPreprint
Zang, X., Wang, T., Zhang, X., Gong, J. R., Gao, P., & Zhang, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4797431
Keyed randomization with adversarial failure curves and moving target defense
Bergadano, F., Gupta, S., & Bruno Crispo, A. (2025) · Intelligent Cybersecurity Conference (ICSC) · doi:10.1109/icsc65596.2025.11140525
Robust Network Intrusion Detection via Semi-supervised Deep Reinforcement Learning
Spolaor, R., Chen, T., Hu, P., & Cheng, X. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-94455-0_9
Duy, P. T., Cuong, N. M., Vy, H. T. Y., Luong, L. T., Anh, N. G. T., Khoa, N. H., & Pham, V.-H. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108019
A Survey of Adversarial Machine Learning Attacks in Unmanned Aircraft System Traffic Management
Shi, Q., Caleb, T. D., Shao, S., & Kaabouch, N. (2025) · Cyber Awareness and Research Symposium (CARS) · doi:10.1109/cars67163.2025.11337414
Reinforcement Learning for the Optimization of Adaptive Intrusion Detection Systems
Gutiérrez, Ó. M., García, D. E., Núñez, J. C. S., & DeCastro‐García, N. (2026) · Summer School on Artificial Intelligence in Cybersecurity · doi:10.3390/engproc2026123002
Pandiyakumari, S. H., & Suganya, R. (2025) · International Conference on Communication, Computing and Industry 4.0 (C2I4) · doi:10.1109/c2i666499.2025.11366957
A deep reinforcement based echo state network for network intrusion classification
Alam, K., Bhuiyan, M. H., & Farid, D. M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0333038
Adversarial traffic generation with legality preservation: A hybrid policy-based framework
Huang, Y., Li, X., Geng, J., Zhu, X., & Chen, Y. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112376
A Multi-Frequency Temporal Spatio-Transformer for adversarially robust IoT intrusion detection
Aljuhani, A., Javeed, D., Alamri, A., & Kumar, P. (2026) · Computers & Security · doi:10.1016/j.cose.2026.104999
Adversarial Reinforcement Learning-Based Intrusion Detection System For Internet of Vehicles
Raj, A., & Kakkar, D. (2026) · International Conference on Secure Cyber Computing and Communications (ICSCCC) · doi:10.1109/icsccc69031.2026.11600306
Neto, E. C. P., Iqbal, S., Buffett, S., Sultana, M., & Taylor, A. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01290-6
Hive-AI: a defended multi-service honeypot framework for generative AI APIs
Yáñez, S. V., & Tobón, S. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01307-0 · Cited by 2
A bibliometric analysis of adversarial attacks and countermeasures in AI/ML cybersecurity
Yazdanjue, N., Itkonen, J., Niemelä, M., & Gandomi, A. H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-28940-8.00007-5
김도완, Jang, J., Nam, S., & Choi, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7488622
AI-Driven Cyber Defense Architectures for Mitigating Generative Adversarial Attacks
Abdullah, S. K., Al-Dulaimi, M. K. H., & Ivakhnenko, M. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-39711-9_1
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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