Application of deep reinforcement learning to intrusion detection for supervised problems
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A deep RL detection system can enhance the security of an IoT system.
“In other cases, a deep RL detection system can be used to enhance the security of an IoT system [150].”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [150]
Cites
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381 · see citing works
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
An introduction to deep reinforcement learning
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018) · doi:10.1561/9781680835397 · Cited by 544 · see citing works
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Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766 · see citing works
Alalwany, E., & Mahgoub, I. (2024) · Sensors · doi:10.3390/s24020368 · Cited by 130 · see citing works
Towards Learning-automation IoT Attack Detection through Reinforcement Learning
Gu, T., Abhishek, A., Fu, H., Zhang, H., Basu, D., & Mohapatra, P. (2020) · 2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM) · doi:10.1109/wowmom49955.2020.00029 · Cited by 50 · see citing works
Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective
Sarker, I. H. (2021) · doi:10.20944/preprints202102.0340.v1 · Cited by 295 · see citing works
- Authors
- Lopez-Martin, M., Carro, B., & Sanchez-Esguevillas, A.
- ORCID
- Lopez-Martin, Manuel
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 141
- Pages
- 112963
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2973862992
- Cited by
- 360
- References
- 4
- Citations by year
- 2026: 38 · 2025: 60 · 2024: 48 · 2023: 75 · 2022: 55 · 2021: 60 · 2020: 23 · 2019: 1
- FWCI
- 18.37
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- Hispana (submittedVersion, cc-by-nc-nd)
- UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- intrusion detection, deep reinforcement learning, supervised learning, reward function design, pseudo-environment, double deep Q-network, policy gradient, actor-critic, NSL-KDD dataset, network security, cyberattacks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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