{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:39:42Z","timestamp":1782833982710,"version":"3.54.5"},"reference-count":129,"publisher":"Association for Computing Machinery (ACM)","issue":"10","license":[{"start":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T00:00:00Z","timestamp":1719014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>Intrusion Detection Systems (IDSs) are an essential element of modern cyber defense, alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to further distinguish between benign and malicious behaviors, but it comes with several challenges, including lack of quality training data and high false-positive rates. Generative Machine Learning Models (GMLMs) can help overcome these challenges. This article offers an in-depth exploration of GMLMs\u2019 application to intrusion detection. It gives (1) a systematic mapping study of research at the intersection of GMLMs and IDSs, and (2) a detailed review providing insights and directions for future research.<\/jats:p>","DOI":"10.1145\/3659575","type":"journal-article","created":{"date-parts":[[2024,4,20]],"date-time":"2024-04-20T10:23:37Z","timestamp":1713608617000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":33,"title":["Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6095-9972","authenticated-orcid":false,"given":"James","family":"Halvorsen","sequence":"first","affiliation":[{"name":"School of EECS, Washington State University, Pullman, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1002-3906","authenticated-orcid":false,"given":"Clemente","family":"Izurieta","sequence":"additional","affiliation":[{"name":"School of Computing, Montana State University, Bozeman, United States and Idaho National Laboratory, Idaho Falls, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5224-9970","authenticated-orcid":false,"given":"Haipeng","family":"Cai","sequence":"additional","affiliation":[{"name":"School of EECS, Washington State University, Pullman, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5383-8032","authenticated-orcid":false,"given":"Assefaw","family":"Gebremedhin","sequence":"additional","affiliation":[{"name":"School of EECS, Washington State University, Pullman, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2021.102828"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1049\/ic.2012.0005"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2018.8646424"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40294-020-00070-w"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851808"},{"issue":"1","key":"e_1_3_1_7_2","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"An Jinwon","year":"2015","unstructured":"Jinwon An and Sungzoon Cho. 2015. Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE 2, 1 (2015), 1\u201318.","journal-title":"Special Lecture on IE"},{"key":"e_1_3_1_8_2","article-title":"Computer Security Threat Monitoring and Surveillance","author":"Anderson James P.","year":"1980","unstructured":"James P. Anderson. 1980. Computer Security Threat Monitoring and Surveillance. Technical Report. James P. Anderson Company.","journal-title":"Technical Report. James P. Anderson Company."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2991079.2991111"},{"key":"e_1_3_1_10_2","unstructured":"Martin Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning. 214\u2013223. http:\/\/proceedings.mlr.press\/v70\/arjovsky17a.html"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13986-4_23"},{"key":"e_1_3_1_12_2","article-title":"BEGAN: Boundary equilibrium generative adversarial networks","volume":"1703","author":"Berthelot David","year":"2017","unstructured":"David Berthelot, Tom Schumm, and Luke Metz. 2017. BEGAN: Boundary equilibrium generative adversarial networks. arXiv abs\/1703.10717 (2017).","journal-title":"arXiv"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00204"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","unstructured":"Ali Borji. 2022. Generated faces in the wild: Quantitative comparison of stable diffusion midjourney and DALL-E 2. arXiv:2210.00586 (2022). 10.48550\/ARXIV.2210.00586","DOI":"10.48550\/ARXIV.2210.00586"},{"key":"e_1_3_1_15_2","volume-title":"Proceedings of the Conference on Cyber Security Experimentation and Test","author":"Brauckhoff Daniela","year":"2008","unstructured":"Daniela Brauckhoff, Arno Wagner, and Martin May. 2008. FLAME: A flow-level anomaly modeling engine. In Proceedings of the Conference on Cyber Security Experimentation and Test."},{"key":"e_1_3_1_16_2","first-page":"1877","volume-title":"Advances in Neural Information Processing Systems","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, 1877\u20131901. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-08600-2"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","unstructured":"Florinel-Alin Croitoru Vlad Hondru Radu Tudor Ionescu and Mubarak Shah. 2022. Diffusion models in vision: A survey. arXiv:2209.04747 (2022). 10.48550\/ARXIV.2209.04747","DOI":"10.48550\/ARXIV.2209.04747"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3198072"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.giq.2017.02.007"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/e21060541"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC51071.2022.9771793"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","unstructured":"Carl Doersch. 2016. Tutorial on variational autoencoders. arXiv:1606.05908 (2016). 10.48550\/ARXIV.1606.05908","DOI":"10.48550\/ARXIV.1606.05908"},{"key":"e_1_3_1_24_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations (ILCR\u201917): Conference Track","author":"Donahue Jeff","year":"2017","unstructured":"Jeff Donahue, Philipp Kr\u00e4henb\u00fchl, and Trevor Darrell. 2017. Adversarial feature learning. In Proceedings of the 5th International Conference on Learning Representations (ILCR\u201917): Conference Track. https:\/\/openreview.net\/forum?id=BJtNZAFgg"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/UEMCON51285.2020.9298135"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9014102"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.12.030"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3024800"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"e_1_3_1_30_2","unstructured":"Karol Gregor Ivo Danihelka Andriy Mnih Charles Blundell and Daan Wierstra. 2014. Deep autoregressive networks. In Proceedings of the 31st International Conference on Machine Learning. 1242\u20131250. https:\/\/proceedings.mlr.press\/v32\/gregor14.html"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-51630-6_6"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295327"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.14569\/IJARAI.2015.040302"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/RISP.1990.63859"},{"key":"e_1_3_1_35_2","first-page":"6840","volume-title":"Advances in Neural Information Processing Systems","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, 6840\u20136851. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf"},{"key":"e_1_3_1_36_2","doi-asserted-by":"crossref","unstructured":"Joerg Hoffmann. 2015. Simulated Penetration Testing: From \u201cDijkstra\u201d to \u201cTuring Test++.\u201d Retrieved April 25 2024 from https:\/\/www.aaai.org\/ocs\/index.php\/ICAPS\/ICAPS15\/paper\/view\/10495","DOI":"10.1609\/icaps.v25i1.13684"},{"key":"e_1_3_1_37_2","unstructured":"Weiwei Hu and Ying Tan. 2017. Generating adversarial malware examples for black-box attacks based on GAN. arXiv:1702.05983 [cs.LG] (2017)."},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33720-9_44"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.11591\/ijeecs.v25.i2.pp1140-1150"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/WINCOM59760.2023.10322987"},{"key":"e_1_3_1_41_2","first-page":"101","article-title":"Deep convolutional generative adversarial networks for in-tent-based dynamic behavior capture","volume":"7","author":"Jan Salman","year":"2018","unstructured":"Salman Jan, Shahrulniza Musa, Toqeer Syed, and Ali Alzahrani. 2018. Deep convolutional generative adversarial networks for in-tent-based dynamic behavior capture. International Journal of Engineering and Technology 7 (2018), 101\u2013103.","journal-title":"International Journal of Engineering and Technology"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3102329"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/DESSERT.2018.8409169"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIIC.2019.8669079"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22155822"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1186\/s42400-019-0038-7"},{"key":"e_1_3_1_47_2","unstructured":"Hyeongju Kim Hyeonseung Lee Woo Hyun Kang Sung Jun Cheon Byoung Jin Choi and Nam Soo Kim. 2020. WaveNODE: A continuous normalizing flow for speech synthesis. arXiv:2006.04598 [cs.SD] (2020)."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70087-8_58"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.04.092"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational Bayes. arXiv:1312.6114 (2013). 10.48550\/ARXIV.1312.6114","DOI":"10.48550\/ARXIV.1312.6114"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.5120\/13608-1412"},{"key":"e_1_3_1_52_2","first-page":"25","article-title":"Normalizing Flows: Introduction and ideas","volume":"1050","author":"Kobyzev Ivan","year":"2019","unstructured":"Ivan Kobyzev, Simon Prince, and Marcus A. Brubaker. 2019. Normalizing Flows: Introduction and ideas. Stat 1050 (2019), 25.","journal-title":"Stat"},{"key":"e_1_3_1_53_2","article-title":"Videoflow: A flow-based generative model for video","author":"Kumar Manoj","year":"2019","unstructured":"Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, and Durk Kingma. 2019. Videoflow: A flow-based generative model for video. arXiv preprint arXiv:1903.01434 (2019).","journal-title":"arXiv preprint arXiv:1903.01434"},{"key":"e_1_3_1_54_2","doi-asserted-by":"crossref","unstructured":"David Kushner. 2013. The real story of Stuxnet: How Kaspersky Lab tracked down the malware that stymied Iran\u2019s nuclear-fuel enrichment program. IEEE Spectrum. Retrieved November 24 2019 from https:\/\/spectrum.ieee.org\/telecom\/security\/the-real-story-of-stuxnet","DOI":"10.1109\/MSPEC.2013.6471059"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110585"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2016.2631891"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2012.09.004"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3149295"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-05981-0_7"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.3390\/app9204396"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-018-1306-7"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17091967"},{"key":"e_1_3_1_63_2","volume-title":"IDES: The Enhanced Prototype. A Real-Time Intrusion Detection Expert System","author":"Lunt Teresa F.","year":"1988","unstructured":"Teresa F. Lunt, R. Jagannathan, Rosanna Lee, Sherry Listgarten, David L. Edwards, Peter G. Neumann, Herald S. Javitz, and L. Valdes. 1988. IDES: The Enhanced Prototype. A Real-Time Intrusion Detection Expert System. SRI International. Computer Science Laboratory."},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSE53436.2021.00033"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.119892"},{"key":"e_1_3_1_66_2","volume-title":"Proceedings of the NATO STO SAS-139 Workshop","author":"Ma\u0142owidzki Marek","year":"2015","unstructured":"Marek Ma\u0142owidzki, Przemyslaw Berezinski, and Micha\u0142 Mazur. 2015. Network intrusion detection: Half a kingdom for a good dataset. In Proceedings of the NATO STO SAS-139 Workshop."},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0016-0032(96)00063-4"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-24344-9_8"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISGT-Europe47291.2020.9248967"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-99587-4_24"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-017-0944-y"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2021.3063538"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8512470"},{"key":"e_1_3_1_75_2","unstructured":"OpenAI. 2023. GPT-4 technical report. arxiv:2303.08774 [cs.CL] (2023)."},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66399-9_19"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2817387"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.092"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.1145\/2601248.2601256"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.14236\/ewic\/EASE2008.8"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","unstructured":"Aditya Ramesh Prafulla Dhariwal Alex Nichol Casey Chu and Mark Chen. 2022. Hierarchical text-conditional image generation with CLIP latents. arXiv:2204.06125 (2022). 10.48550\/ARXIV.2204.06125","DOI":"10.48550\/ARXIV.2204.06125"},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2018.00019"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2018.12.012"},{"key":"e_1_3_1_84_2","unstructured":"Markus Ring Sarah Wunderlich Dominik Gr\u00fcdl Dieter Landes and Andreas Hotho. 2017. Flow-based benchmark data sets for intrusion detection. In Proceedings of the European Conference on Cyber Warfare and Security (ECCWS\u201917). 1\u201310."},{"key":"e_1_3_1_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_1_86_2","doi-asserted-by":"publisher","unstructured":"Royi Ronen Marian Radu Corina Feuerstein Elad Yom-Tov and Mansour Ahmadi. 2018. Microsoft Malware Classification Challenge. arXiv:1802.10135 (2018). 10.48550\/ARXIV.1802.10135","DOI":"10.48550\/ARXIV.1802.10135"},{"key":"e_1_3_1_87_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-7166-0_14"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.1109\/UEMCON.2018.8796769"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/PRDC50213.2020.00018"},{"key":"e_1_3_1_90_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC48688.2020.0-218"},{"key":"e_1_3_1_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/CNS53000.2021.9705034"},{"key":"e_1_3_1_92_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00136"},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.5220\/0006639801080116"},{"key":"e_1_3_1_94_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSPIT.2018.8642683"},{"key":"e_1_3_1_95_2","doi-asserted-by":"publisher","DOI":"10.1080\/1206212X.2021.1885150"},{"key":"e_1_3_1_96_2","unstructured":"Zhanna Malekos Smith Eugenia Lostri and James A Lewis. 2020. The hidden costs of cybercrime. Trellix. Retrieved April 25 2024 from https:\/\/www.mcafee.com\/enterprise\/en-us\/assets\/reports\/rp-hidden-costs-of-cybercrime.pdf"},{"key":"e_1_3_1_97_2","volume-title":"Advances in Neural Information Processing Systems","author":"Sohn Kihyuk","year":"2015","unstructured":"Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. In Advances in Neural Information Processing Systems, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett (Eds.). Vol. 28. Curran Associates, 1\u20139.https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/8d55a249e6baa5c06772297520da2051-Paper.pdf"},{"key":"e_1_3_1_98_2","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295110"},{"key":"e_1_3_1_99_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.10.004"},{"key":"e_1_3_1_100_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2848210"},{"key":"e_1_3_1_101_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2821095"},{"key":"e_1_3_1_102_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00130487"},{"key":"e_1_3_1_103_2","volume-title":"Synthesizing Cyber Intrusion Alerts Using Generative Adversarial Networks","author":"Sweet Christopher","year":"2019","unstructured":"Christopher Sweet. 2019. Synthesizing Cyber Intrusion Alerts Using Generative Adversarial Networks. Master\u2019s Thesis. Rochester Institute of Technology."},{"key":"e_1_3_1_104_2","doi-asserted-by":"publisher","DOI":"10.1109\/MILCOM47813.2019.9020850"},{"key":"e_1_3_1_105_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394503"},{"key":"e_1_3_1_106_2","unstructured":"Wesley Tann Yuancheng Liu Jun Heng Sim Choon Meng Seah and Ee-Chien Chang. 2023. Using large language models for cybersecurity Capture-The-Flag challenges and certification questions. arxiv:2308.10443 [cs.AI] (2023)."},{"key":"e_1_3_1_107_2","doi-asserted-by":"publisher","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"e_1_3_1_108_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISNCC52172.2021.9615643"},{"key":"e_1_3_1_109_2","doi-asserted-by":"publisher","DOI":"10.1109\/iSPEC53008.2021.9735442"},{"key":"e_1_3_1_110_2","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2019.8766353"},{"key":"e_1_3_1_111_2","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Vahdat Arash","year":"2020","unstructured":"Arash Vahdat and Jan Kautz. 2020. NVAE: A deep hierarchical variational autoencoder. In Proceedings of the 34th International Conference on Neural Information Processing Systems(NIPS\u201920). Article 1650, 13 pages."},{"key":"e_1_3_1_112_2","volume-title":"Advances in Neural Information Processing Systems","author":"Oord Aaron van den","year":"2016","unstructured":"Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, and Alex Graves. 2016. Conditional image generation with PixelCNN decoders. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29. Curran Associates, 1\u20139.https:\/\/proceedings.neurips.cc\/paper\/2016\/file\/b1301141feffabac455e1f90a7de2054-Paper.pdf"},{"key":"e_1_3_1_113_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2895334"},{"key":"e_1_3_1_114_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2017.7510583"},{"key":"e_1_3_1_115_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87839-9_1"},{"key":"e_1_3_1_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3034621"},{"key":"e_1_3_1_117_2","doi-asserted-by":"publisher","unstructured":"Tijin Yan Tong Zhou Yufeng Zhan and Yuanqing Xia. 2021. TFDPM: Attack detection for cyber-physical systems with diffusion probabilistic models. arXiv:2112.10774 (2021). 10.48550\/ARXIV.2112.10774","DOI":"10.48550\/ARXIV.2112.10774"},{"key":"e_1_3_1_118_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3083422"},{"key":"e_1_3_1_119_2","unstructured":"Li-Chia Yang Szu-Yu Chou and Yi-Hsuan Yang. 2017. MidiNet: A convolutional generative adversarial network for symbolic-domain music generation using 1D and 2D conditions. arXiv:1703.10847 (2017)."},{"key":"e_1_3_1_120_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977007"},{"key":"e_1_3_1_121_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19112528"},{"key":"e_1_3_1_122_2","doi-asserted-by":"publisher","DOI":"10.1109\/IRI49571.2020.00012"},{"key":"e_1_3_1_123_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIBD.2018.8396200"},{"key":"e_1_3_1_124_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544216.3544251"},{"key":"e_1_3_1_125_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09717-4"},{"key":"e_1_3_1_126_2","unstructured":"Lantao Yu Weinan Zhang Jun Wang and Yong Yu. 2016. SeqGAN: Sequence generative adversarial nets with policy gradient. arxiv:1609.05473 [cs.LG] (2016)."},{"key":"e_1_3_1_127_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3001350"},{"key":"e_1_3_1_128_2","article-title":"Image de-raining using a conditional generative adversarial network","author":"Zhang H.","year":"2019","unstructured":"H. Zhang, V. Sindagi, and V. M. Patel. 2019. Image de-raining using a conditional generative adversarial network. IEEE Transactions on Circuits and Systems for Video Technology. Published Online, June 3, 2019.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology."},{"key":"e_1_3_1_129_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations (ICLR\u201917): Conference Track","author":"Zhao Junbo Jake","year":"2017","unstructured":"Junbo Jake Zhao, Micha\u00ebl Mathieu, and Yann LeCun. 2017. Energy-based generative adversarial networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR\u201917): Conference Track. https:\/\/openreview.net\/forum?id=ryh9pmcee"},{"key":"e_1_3_1_130_2","doi-asserted-by":"publisher","DOI":"10.1109\/IEMCON51383.2020.9284901"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3659575","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3659575","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:42Z","timestamp":1750291422000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3659575"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,22]]},"references-count":129,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10,31]]}},"alternative-id":["10.1145\/3659575"],"URL":"https:\/\/doi.org\/10.1145\/3659575","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,22]]},"assertion":[{"value":"2023-01-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-31","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}