{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T20:38:45Z","timestamp":1773693525329,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Cyber Security Research Programme\u2014Artificial Intelligence for Automating Response to Threats from the Ministry of Business, Innovation, and Employment (MBIE) of New Zealand","award":["MAUX1912"],"award-info":[{"award-number":["MAUX1912"]}]},{"name":"Massey University\u2014Massey University Research Fund Early Career Round","award":["MAUX1912"],"award-info":[{"award-number":["MAUX1912"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Existing 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 \u201cfool\u201d 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\u2013discriminator. 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.<\/jats:p>","DOI":"10.3390\/computers11060085","type":"journal-article","created":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T08:50:22Z","timestamp":1653555022000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Improved Bidirectional GAN-Based Approach for Network Intrusion Detection Using One-Class Classifier"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0558-9558","authenticated-orcid":false,"given":"Wen","family":"Xu","sequence":"first","affiliation":[{"name":"Cybersecurity Lab, Comp Sci\/Info Tech, Massey University, Auckland 0632, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1002-057X","authenticated-orcid":false,"given":"Julian","family":"Jang-Jaccard","sequence":"additional","affiliation":[{"name":"Cybersecurity Lab, Comp Sci\/Info Tech, Massey University, Auckland 0632, New Zealand"}]},{"given":"Tong","family":"Liu","sequence":"additional","affiliation":[{"name":"Cybersecurity Lab, Comp Sci\/Info Tech, Massey University, Auckland 0632, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8455-2499","authenticated-orcid":false,"given":"Fariza","family":"Sabrina","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6931-2705","authenticated-orcid":false,"given":"Jin","family":"Kwak","sequence":"additional","affiliation":[{"name":"Department of Cyber Security, Ajou University, Suwon 16499, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/j.jcss.2014.02.005","article-title":"A survey of emerging threats in cybersecurity","volume":"80","author":"Nepal","year":"2014","journal-title":"J. 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