Improved Bidirectional GAN-Based Approach for Network Intrusion Detection Using One-Class Classifier

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dc.citation.issue6
dc.citation.volume11
dc.contributor.authorXu W
dc.contributor.authorJang-Jaccard J
dc.contributor.authorLiu T
dc.contributor.authorSabrina F
dc.contributor.authorKwak J
dc.date.accessioned2023-11-20T01:37:52Z
dc.date.available2022-06-01
dc.date.available2023-11-20T01:37:52Z
dc.date.issued2022-06-01
dc.description.abstractExisting generative adversarial networks (GANs), primarily used for creating fake image samples from natural images, demand a strong dependence (i.e., the training strategy of the generators and the discriminators require to be in sync) for the generators to produce as realistic fake samples that can “fool” the discriminators. We argue that this strong dependency required for GAN training on images does not necessarily work for GAN models for network intrusion detection tasks. This is because the network intrusion inputs have a simpler feature structure such as relatively low-dimension, discrete feature values, and smaller input size compared to the existing GAN-based anomaly detection tasks proposed on images. To address this issue, we propose a new Bidirectional GAN (Bi-GAN) model that is better equipped for network intrusion detection with reduced overheads involved in excessive training. In our proposed method, the training iteration of the generator (and accordingly the encoder) is increased separate from the training of the discriminator until it satisfies the condition associated with the cross-entropy loss. Our empirical results show that this proposed training strategy greatly improves the performance of both the generator and the discriminator even in the presence of imbalanced classes. In addition, our model offers a new construct of a one-class classifier using the trained encoder–discriminator. The one-class classifier detects anomalous network traffic based on binary classification results instead of calculating expensive and complex anomaly scores (or thresholds). Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on two datasets: NSL-KDD and CIC-DDoS2019 datasets.
dc.description.publication-statusPublished
dc.identifier.citationComputers, 2022, 11 (6)
dc.identifier.doi10.3390/computers11060085
dc.identifier.eissn2073-431X
dc.identifier.elements-id454552
dc.identifier.harvestedMassey_Dark
dc.identifier.urihttps://hdl.handle.net/10179/17423
dc.publisherMDPI (Basel, Switzerland)
dc.relation.isPartOfComputers
dc.rightsCC BY 4.0
dc.titleImproved Bidirectional GAN-Based Approach for Network Intrusion Detection Using One-Class Classifier
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Mathematical and Computational Sciences
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