Deep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection

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dc.citation.issue3
dc.citation.volume11
dc.contributor.authorAlavizadeh H
dc.contributor.authorAlavizadeh H
dc.contributor.authorJang-Jaccard J
dc.contributor.editorQuaresma P
dc.contributor.editorNogueira V
dc.contributor.editorSaias J
dc.date.accessioned2023-11-14T22:11:24Z
dc.date.accessioned2023-11-20T01:38:39Z
dc.date.available2022-03-11
dc.date.available2023-11-14T22:11:24Z
dc.date.available2023-11-20T01:38:39Z
dc.date.issued2022-03-11
dc.description.abstractThe rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of the network intrusion detection method, which combines a Q-learning based reinforcement learning with a deep feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learning (DQL) model provides an ongoing auto-learning capability for a network environment that can detect different types of network intrusions using an automated trial-error approach and continuously enhance its detection capabilities. We provide the details of fine-tuning different hyperparameters involved in the DQL model for more effective self-learning. According to our extensive experimental results based on the NSL-KDD dataset, we confirm that the lower discount factor, which is set as 0.001 under 250 episodes of training, yields the best performance results. Our experimental results also show that our proposed DQL is highly effective in detecting different intrusion classes and outperforms other similar machine learning approaches.
dc.description.confidentialfalse
dc.edition.editionMarch 2022
dc.identifier.citationAlavizadeh H, Alavizadeh H, Jang-Jaccard J. (2022). Deep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection. Computers. 11. 3.
dc.identifier.doi10.3390/computers11030041
dc.identifier.eissn2073-431X
dc.identifier.elements-typejournal-article
dc.identifier.number41
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69207
dc.languageEnglish
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/2073-431X/11/3/41
dc.relation.isPartOfComputers
dc.rights(c) 2022 The Author/s
dc.rightsCC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectnetwork security
dc.subjectdeep Q networks
dc.subjectdeep learning
dc.subjectreinforcement learning
dc.subjectnetwork intrusion detection
dc.subjectNSL-KDD
dc.subjectartificial intelligence
dc.titleDeep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection
dc.typeJournal article
pubs.elements-id452606
pubs.organisational-groupOther
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