{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:24:58Z","timestamp":1774365898115,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3-4","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"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":["Ann. Telecommun."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s12243-024-01021-9","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T08:02:23Z","timestamp":1711526543000},"page":"209-226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Generating practical adversarial examples against learning-based network intrusion detection systems"],"prefix":"10.1007","volume":"80","author":[{"given":"Vivek","family":"Kumar","sequence":"first","affiliation":[]},{"given":"Kamal","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Maheep","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"1021_CR1","doi-asserted-by":"crossref","unstructured":"Javaid A, Niyaz Q, Sun W, Alam M (2016) A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), pp 21\u201326","DOI":"10.4108\/eai.3-12-2015.2262516"},{"issue":"3","key":"1021_CR2","first-page":"35","volume":"1","author":"V Kumar","year":"2012","unstructured":"Kumar V, Sangwan OP (2012) Signature based intrusion detection system using snort. Int J Comput Appl Inf Technol 1(3):35\u201341","journal-title":"Int J Comput Appl Inf Technol"},{"key":"1021_CR3","doi-asserted-by":"publisher","first-page":"22351","DOI":"10.1109\/ACCESS.2021.3056614","volume":"9","author":"ZK Maseer","year":"2021","unstructured":"Maseer ZK, Yusof R, Bahaman N, Mostafa SA, Foozy CFM (2021) Benchmarking of machine learning for anomaly based intrusion detection systems in the cicids2017 dataset. IEEE Access 9:22351\u201322370","journal-title":"IEEE Access"},{"key":"1021_CR4","doi-asserted-by":"crossref","unstructured":"Parrend P, Navarro J, Guigou F, Deruyver A, Collet P (2018) Foundations and applications of artificial intelligence for zero-day and multi-step attack detection. EURASIP J Inf Secur 2018(1):1\u201321","DOI":"10.1186\/s13635-018-0074-y"},{"key":"1021_CR5","doi-asserted-by":"crossref","unstructured":"Chiba Z, Abghour N, Moussaid K, Rida M et al (2019) Intelligent approach to build a deep neural network based ids for cloud environment using combination of machine learning algorithms. Comput Secur 86:291\u2013317","DOI":"10.1016\/j.cose.2019.06.013"},{"issue":"2","key":"1021_CR6","doi-asserted-by":"publisher","first-page":"4014","DOI":"10.1002\/ett.4014","volume":"32","author":"I Sumaiya Thaseen","year":"2021","unstructured":"Sumaiya Thaseen I, Saira Banu J, Lavanya K, Rukunuddin Ghalib M, Abhishek K (2021) An integrated intrusion detection system using correlation-based attribute selection and artificial neural network. Trans Emerg Telecommun Technol 32(2):4014","journal-title":"Trans Emerg Telecommun Technol"},{"key":"1021_CR7","doi-asserted-by":"publisher","first-page":"138432","DOI":"10.1109\/ACCESS.2021.3118573","volume":"9","author":"T Wisanwanichthan","year":"2021","unstructured":"Wisanwanichthan T, Thammawichai M (2021) A double-layered hybrid approach for network intrusion detection system using combined naive bayes and SVM. IEEE Access 9:138432\u2013138450","journal-title":"IEEE Access"},{"issue":"7","key":"1021_CR8","doi-asserted-by":"publisher","first-page":"751","DOI":"10.3390\/math9070751","volume":"9","author":"R Panigrahi","year":"2021","unstructured":"Panigrahi R, Borah S, Bhoi AK, Ijaz MF, Pramanik M, Kumar Y, Jhaveri RH (2021) A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets. Mathematics 9(7):751","journal-title":"Mathematics"},{"key":"1021_CR9","doi-asserted-by":"crossref","unstructured":"Benaddi H, Ibrahimi K, Benslimane A (2018) Improving the intrusion detection system for NSL-KDD dataset based on PCA-fuzzy clustering-KNN. In: 2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM), pp 1\u20136. IEEE","DOI":"10.1109\/WINCOM.2018.8629718"},{"key":"1021_CR10","doi-asserted-by":"crossref","unstructured":"Seo E, Song HM, Kim HK (2018) GIDS: GAN based intrusion detection system for in-vehicle network. In: 2018 16th Annual Conference on Privacy, Security and Trust (PST), pp 1\u20136. IEEE","DOI":"10.1109\/PST.2018.8514157"},{"key":"1021_CR11","first-page":"1633","volume":"33","author":"F Tramer","year":"2020","unstructured":"Tramer F, Carlini N, Brendel W, Madry A (2020) On adaptive attacks to adversarial example defenses. Adv Neural Inf Process Syst 33:1633\u20131645","journal-title":"Adv Neural Inf Process Syst"},{"issue":"2","key":"1021_CR12","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.1109\/TGRS.2020.2999962","volume":"59","author":"Y Xu","year":"2020","unstructured":"Xu Y, Du B, Zhang L (2020) Assessing the threat of adversarial examples on deep neural networks for remote sensing scene classification: attacks and defenses. IEEE Trans Geosci Remote Sens 59(2):1604\u20131617","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1021_CR13","unstructured":"Wiyatno RR, Xu A, Dia O, de Berker A (2019) Adversarial examples in modern machine learning: a review. arXiv:1911.05268"},{"key":"1021_CR14","unstructured":"Alatwi HA, Morisset C (2021) Adversarial machine learning in network intrusion detection domain: a systematic review. arXiv:2112.03315"},{"key":"1021_CR15","doi-asserted-by":"crossref","unstructured":"Sheatsley R, Hoak B, Pauley E, Beugin Y, Weisman MJ, McDaniel P (2021) On the robustness of domain constraints. In: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pp 495\u2013515","DOI":"10.1145\/3460120.3484570"},{"key":"1021_CR16","unstructured":"Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. arXiv:1312.6199"},{"key":"1021_CR17","doi-asserted-by":"crossref","unstructured":"Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A (2016) The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS &P), pp 372\u2013387. IEEE","DOI":"10.1109\/EuroSP.2016.36"},{"key":"1021_CR18","doi-asserted-by":"crossref","unstructured":"Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In: 2017 Ieee Symposium on Security and Privacy (sp), pp 39\u201357. IEEE","DOI":"10.1109\/SP.2017.49"},{"key":"1021_CR19","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli S-M, Fawzi A, Frossard P (2016) Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2574\u20132582","DOI":"10.1109\/CVPR.2016.282"},{"key":"1021_CR20","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv:1706.06083 (2017)"},{"key":"1021_CR21","doi-asserted-by":"crossref","unstructured":"Kurakin A, Goodfellow IJ, Bengio S (2018) Adversarial examples in the physical world. In: Artificial Intelligence Safety and Security, pp 99\u2013112. Chapman and Hall\/CRC, ???","DOI":"10.1201\/9781351251389-8"},{"key":"1021_CR22","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli S-M, Fawzi A, Fawzi O, Frossard P (2017) Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1765\u20131773","DOI":"10.1109\/CVPR.2017.17"},{"key":"1021_CR23","doi-asserted-by":"crossref","unstructured":"Chen J, Wu D, Zhao Y, Sharma N, Blumenstein M, Yu S (2021) Fooling intrusion detection systems using adversarially autoencoder. Digit Commun Netw 7(3):453\u2013460","DOI":"10.1016\/j.dcan.2020.11.001"},{"key":"1021_CR24","doi-asserted-by":"crossref","unstructured":"Usama M, Asim M, Latif S, Qadir J et al (2019) Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems. In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp 78\u201383. IEEE","DOI":"10.1109\/IWCMC.2019.8766353"},{"key":"1021_CR25","doi-asserted-by":"crossref","unstructured":"Zhong Y, Zhu Y, Wang Z, Yin X, Shi X, Li K (2020) An adversarial learning model for intrusion detection in real complex network environments. In: International Conference on Wireless Algorithms, Systems, and Applications, pp 794\u2013806. Springer","DOI":"10.1007\/978-3-030-59016-1_65"},{"issue":"1","key":"1021_CR26","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3390\/sym14010066","volume":"14","author":"C-S Shieh","year":"2022","unstructured":"Shieh C-S, Nguyen T-T, Lin W-W, Huang Y-L, Horng M-F, Lee T-F, Miu D (2022) Detection of adversarial DDoS attacks using generative adversarial networks with dual discriminators. Symmetry 14(1):66","journal-title":"Symmetry"},{"key":"1021_CR27","unstructured":"Cheng Q, Zhou S, Shen Y, Kong D, Wu C (2021) Packet-level adversarial network traffic crafting using sequence generative adversarial networks. arXiv:2103.04794"},{"key":"1021_CR28","doi-asserted-by":"crossref","unstructured":"Lin Z, Shi Y, Xue Z (2022) Idsgan: generative adversarial networks for attack generation against intrusion detection. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp 79\u201391. Springer","DOI":"10.1007\/978-3-031-05981-0_7"},{"key":"1021_CR29","doi-asserted-by":"crossref","unstructured":"Li P, Zhao W, Liu Q, Liu X, Yu L (2018) Poisoning machine learning based wireless IDSs via stealing learning model. In: International Conference on Wireless Algorithms, Systems, and Applications, pp 261\u2013273. Springer","DOI":"10.1007\/978-3-319-94268-1_22"},{"key":"1021_CR30","doi-asserted-by":"crossref","unstructured":"Aiken J, Scott-Hayward S (2019) Investigating adversarial attacks against network intrusion detection systems in sdns. In: 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), pp 1\u20137. IEEE","DOI":"10.1109\/NFV-SDN47374.2019.9040101"},{"key":"1021_CR31","doi-asserted-by":"crossref","unstructured":"Usama M, Qadir J, Al-Fuqaha A, Hamdi M (2019) The adversarial machine learning conundrum: can the insecurity of ml become the Achilles\u2019 heel of cognitive networks? IEEE Netw 34(1):196\u2013203","DOI":"10.1109\/MNET.001.1900197"},{"key":"1021_CR32","doi-asserted-by":"crossref","unstructured":"Peng X, Huang W, Shi Z (2019) Adversarial attack against dos intrusion detection: an improved boundary-based method. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp 1288\u20131295. IEEE","DOI":"10.1109\/ICTAI.2019.00179"},{"key":"1021_CR33","doi-asserted-by":"crossref","unstructured":"Abusnaina A, Khormali A, Nyang D, Yuksel M, Mohaisen A (2019) Examining the robustness of learning-based ddos detection in software defined networks. In: 2019 IEEE Conference on Dependable and Secure Computing (DSC), pp 1\u20138. IEEE","DOI":"10.1109\/DSC47296.2019.8937669"},{"key":"1021_CR34","doi-asserted-by":"crossref","unstructured":"Teuffenbach M, Piatkowska E, Smith P (2020) Subverting network intrusion detection: crafting adversarial examples accounting for domain-specific constraints. In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction, pp 301\u2013320. Springer","DOI":"10.1007\/978-3-030-57321-8_17"},{"key":"1021_CR35","doi-asserted-by":"crossref","unstructured":"Chauhan R, Heydari SS (2020) Polymorphic adversarial DDoS attack on ids using GAN. In: 2020 International Symposium on Networks, Computers and Communications (ISNCC), pp 1\u20136. IEEE","DOI":"10.1109\/ISNCC49221.2020.9297264"},{"key":"1021_CR36","unstructured":"Cheng Q, Zhou S, Shen Y, Kong D, Wu C (2021) Packet-level adversarial network traffic crafting using sequence generative adversarial networks. arXiv:2103.04794"},{"issue":"12","key":"1021_CR37","doi-asserted-by":"publisher","first-page":"3387","DOI":"10.1007\/s13042-019-00925-6","volume":"10","author":"Q Yan","year":"2019","unstructured":"Yan Q, Wang M, Huang W, Luo X, Yu FR (2019) Automatically synthesizing dos attack traces using generative adversarial networks. Int J Mach Learn Cybern 10(12):3387\u20133396","journal-title":"Int J Mach Learn Cybern"},{"key":"1021_CR38","unstructured":"Han D, Wang Z, Zhong Y, Chen W, Yang J, Lu S, Shi X, Yin X (2020) Practical traffic-space adversarial attacks on learning-based nidss. arXiv:2005.07519"},{"key":"1021_CR39","doi-asserted-by":"crossref","unstructured":"Shu D, Leslie NO, Kamhoua CA, Tucker CS (2020) Generative adversarial attacks against intrusion detection systems using active learning. In: Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning, pp 1\u20136","DOI":"10.1145\/3395352.3402618"},{"key":"1021_CR40","doi-asserted-by":"crossref","unstructured":"Merzouk, M.A., Cuppens, F., Boulahia-Cuppens, N., Yaich, R.: Investigating the practicality of adversarial evasion attacks on network intrusion detection. Annals of Telecommunications, 1\u201313 (2022)","DOI":"10.1145\/3538969.3539006"},{"key":"1021_CR41","first-page":"108","volume":"1","author":"I Sharafaldin","year":"2018","unstructured":"Sharafaldin I, Lashkari AH, Ghorbani AA (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1:108\u2013116","journal-title":"ICISSp"},{"key":"1021_CR42","doi-asserted-by":"crossref","unstructured":"Valiant LG (1984) A theory of the learnable. Commun ACM 27(11):1134\u20131142","DOI":"10.1145\/1968.1972"},{"key":"1021_CR43","doi-asserted-by":"crossref","unstructured":"Ankerst M, Breunig MM, Kriegel H-P, Sander J (1999) Optics: ordering points to identify the clustering structure. ACM Sigmod Rec 28(2):49\u201360","DOI":"10.1145\/304181.304187"}],"container-title":["Annals of Telecommunications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12243-024-01021-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12243-024-01021-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12243-024-01021-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T06:58:41Z","timestamp":1741676321000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12243-024-01021-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":43,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["1021"],"URL":"https:\/\/doi.org\/10.1007\/s12243-024-01021-9","relation":{},"ISSN":["0003-4347","1958-9395"],"issn-type":[{"value":"0003-4347","type":"print"},{"value":"1958-9395","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,27]]},"assertion":[{"value":"29 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}