{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T11:05:16Z","timestamp":1784027116594,"version":"3.55.0"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032207319","type":"print"},{"value":"9783032207326","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-20732-6_10","type":"book-chapter","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T10:28:16Z","timestamp":1784024896000},"page":"153-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Domain Adversarial Neural Networks with\u00a0Adversarial Robustness Evaluation for\u00a0Intrusion Detection Systems"],"prefix":"10.1007","author":[{"given":"Ines","family":"Guerziz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiago","family":"Falk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long Bao","family":"Le","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zakaria Abou El","family":"Houda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Mainuddin, M., Duan, Z., Dong, Y.: Network traffic characteristics of IoT devices in smart homes. In: 2021 International Conference on Computer Communications and Networks (ICCCN), pp. 1\u201311. IEEE (2021)","DOI":"10.1109\/ICCCN52240.2021.9522168"},{"issue":"59","key":"10_CR2","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pp. 506\u2013519 (2017)","DOI":"10.1145\/3052973.3053009"},{"key":"10_CR4","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"10_CR5","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"issue":"10","key":"10_CR6","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3390\/computers12100209","volume":"12","author":"M Wang","year":"2023","unstructured":"Wang, M., Yang, N., Gunasinghe, D.H., Weng, N.: On the robustness of ml-based network intrusion detection systems: an adversarial and distribution shift perspective. Computers 12(10), 209 (2023)","journal-title":"Computers"},{"issue":"1","key":"10_CR7","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TDSC.2023.3247585","volume":"21","author":"H Yan","year":"2023","unstructured":"Yan, H., et al.: Automatic evasion of machine learning-based network intrusion detection systems. IEEE Trans. Dependable Secure Comput. 21(1), 153\u2013167 (2023)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1\u20136. IEEE (2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"10_CR9","unstructured":"Jatti, S., Sontif, V.K.: UNSW-NB15: a comprehensive data set for network intrusion detection systems. Int. J. Recent Technol. Eng. (2019)"},{"key":"10_CR10","unstructured":"Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning, pp. 2208\u20132217. PMLR (2017)"},{"key":"10_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_35"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"Hu, W., Tan, Y.: Generating adversarial malware examples for black-box attacks based on GAN. In: International Conference on Data Mining and Big Data, pp. 409\u2013423. Springer (2022). https:\/\/doi.org\/10.1007\/978-981-19-8991-9_29","DOI":"10.1007\/978-981-19-8991-9_29"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Rigaki, M., Garcia, S.: Bringing a GAN to a knife-fight: adapting malware communication to avoid detection. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 70\u201375. IEEE (2018)","DOI":"10.1109\/SPW.2018.00019"},{"key":"10_CR15","unstructured":"Carlini, N., Wagner, D.: Defensive distillation is not robust to adversarial examples. arXiv preprint arXiv:1607.04311 (2016)"},{"key":"10_CR16","unstructured":"Shafahi, A., et al.: Adversarial training for free! In: Advances in Neural Information Processing Systems, vol.\u00a032 (2019)"}],"container-title":["Lecture Notes in Computer Science","Risks and Security of Internet and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-20732-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T10:28:18Z","timestamp":1784024898000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-20732-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032207319","9783032207326"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-20732-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CRiSIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Risks and Security of Internet and Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gatineau, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"crisis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/crisis2025.uqo.ca\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}