{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T13:11:17Z","timestamp":1785503477843,"version":"3.56.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key R&D Program of China, China","award":["2021YFB3101700"],"award-info":[{"award-number":["2021YFB3101700"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s13042-026-03163-9","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T08:38:27Z","timestamp":1779957507000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VPGM: variable perturbation adversarial sample generation method for time-series-based industrial anomaly detection system"],"prefix":"10.1007","volume":"17","author":[{"given":"Ziyong","family":"Ran","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Shan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiming","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"issue":"6","key":"3163_CR1","doi-asserted-by":"publisher","first-page":"1802","DOI":"10.1109\/JIOT.2017.2703172","volume":"4","author":"A Humayed","year":"2017","unstructured":"Humayed A, Lin J, Li F, Luo B (2017) Cyber-physical systems security\u2014a survey. IEEE Internet Things J 4(6):1802\u20131831","journal-title":"IEEE Internet Things J"},{"issue":"9","key":"3163_CR2","doi-asserted-by":"publisher","first-page":"4093","DOI":"10.1109\/TII.2018.2832853","volume":"14","author":"H Mouratidis","year":"2018","unstructured":"Mouratidis H, Diamantopoulou V (2018) A security analysis method for industrial internet of things. IEEE Trans Industr Inf 14(9):4093\u20134100","journal-title":"IEEE Trans Industr Inf"},{"issue":"7","key":"3163_CR3","doi-asserted-by":"publisher","first-page":"4319","DOI":"10.1109\/TII.2022.3141408","volume":"18","author":"X Yang","year":"2022","unstructured":"Yang X, Shu L, Liu Y, Hancke GP, Ferrag MA, Huang K (2022) Physical security and safety of IoT equipment: a survey of recent advances and opportunities. IEEE Trans Ind Inf 18(7):4319\u20134330","journal-title":"IEEE Trans Ind Inf"},{"issue":"2","key":"3163_CR4","doi-asserted-by":"publisher","first-page":"16","DOI":"10.52710\/rjcse.71","volume":"4","author":"YD Deshpande","year":"2023","unstructured":"Deshpande YD, Rahman S (2023) Edge-based real-time sensor data processing for anomaly detection in industrial IoT applications. Res J Comput Syst Eng 4(2):16\u201330","journal-title":"Res J Comput Syst Eng"},{"key":"3163_CR5","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.future.2022.04.005","volume":"134","author":"A Sgueglia","year":"2022","unstructured":"Sgueglia A, Di Sorbo A, Visaggio CA, Canfora G (2022) A systematic literature review of IoT time series anomaly detection solutions. Futur Gener Comput Syst 134:170\u2013186","journal-title":"Futur Gener Comput Syst"},{"issue":"2","key":"3163_CR6","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1109\/TNSE.2018.2830307","volume":"7","author":"Z Cai","year":"2018","unstructured":"Cai Z, Zheng X (2018) A private and efficient mechanism for data uploading in smart cyber-physical systems. IEEE Trans Netw Sci Eng 7(2):766\u2013775","journal-title":"IEEE Trans Netw Sci Eng"},{"issue":"10","key":"3163_CR7","doi-asserted-by":"publisher","first-page":"3309","DOI":"10.1109\/TPAMI.2020.2986319","volume":"43","author":"F Karim","year":"2020","unstructured":"Karim F, Majumdar S, Darabi H (2020) Adversarial attacks on time series. IEEE Trans Pattern Anal Mach Intell 43(10):3309\u20133320","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3163_CR8","doi-asserted-by":"crossref","unstructured":"Zhang K, Wen Q, Zhang C, Cai R, Jin M, Liu Y, Zhang JY, Liang Y, Pang G, Song D et al (2024) Self-supervised learning for time series analysis: taxonomy, progress, and prospects. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2024.3387317"},{"issue":"1","key":"3163_CR9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s10207-024-00977-y","volume":"24","author":"N Mohammadi","year":"2025","unstructured":"Mohammadi N, Rezakhani A, Javadi HHS, Asghari P (2025) Enhancing time-series access control using deep recurrent neural networks and generative adversarial networks. Int J Inf Secur 24(1):61","journal-title":"Int J Inf Secur"},{"key":"3163_CR10","doi-asserted-by":"crossref","unstructured":"Pialla G, Fawaz HI, Devanne M, Weber J, Idoumghar L, Muller P-A, Bergmeir C, Schmidt D, Webb G, Forestier G (2022) Smooth perturbations for time series adversarial attacks. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 485\u2013496","DOI":"10.1007\/978-3-031-05933-9_38"},{"issue":"4","key":"3163_CR11","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1109\/TII.2014.2300753","volume":"10","author":"L Da Xu","year":"2014","unstructured":"Da Xu L, He W, Li S (2014) Internet of things in industries: a survey. IEEE Trans Ind Inf 10(4):2233\u20132243","journal-title":"IEEE Trans Ind Inf"},{"issue":"4","key":"3163_CR12","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1109\/COMST.2020.3011208","volume":"22","author":"K Tange","year":"2020","unstructured":"Tange K, De Donno M, Fafoutis X, Dragoni N (2020) A systematic survey of industrial internet of things security: requirements and fog computing opportunities. IEEE Commun Surv Tutor 22(4):2489\u20132520","journal-title":"IEEE Commun Surv Tutor"},{"key":"3163_CR13","doi-asserted-by":"crossref","unstructured":"Zhang Z, Liu M, Sun M, Deng R, Cheng P, Niyato D, Chow M-Y, Chen J (2024) Vulnerability of machine learning approaches applied in iot-based smart grid: a review. IEEE Internet Things J","DOI":"10.1109\/JIOT.2024.3349381"},{"issue":"3","key":"3163_CR14","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.3390\/app14031194","volume":"14","author":"MF Guato Burgos","year":"2024","unstructured":"Guato Burgos MF, Morato J, Vizcaino Imaca\u00f1a FP (2024) A review of smart grid anomaly detection approaches pertaining to artificial intelligence. Appl Sci 14(3):1194","journal-title":"Appl Sci"},{"issue":"2","key":"3163_CR15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/asi7020018","volume":"7","author":"W-H Choi","year":"2024","unstructured":"Choi W-H, Kim J (2024) Unsupervised learning approach for anomaly detection in industrial control systems. Appl Syst Innov 7(2):18","journal-title":"Appl Syst Innov"},{"issue":"6","key":"3163_CR16","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s10489-025-06412-6","volume":"55","author":"C Gao","year":"2025","unstructured":"Gao C, Ma H, Pei Q, Chen Y (2025) Dynamic graph-based graph attention network for anomaly detection in industrial multivariate time series data. Appl Intell 55(6):517","journal-title":"Appl Intell"},{"issue":"1","key":"3163_CR17","doi-asserted-by":"publisher","first-page":"2334303","DOI":"10.1080\/21642583.2024.2334303","volume":"12","author":"MM Aslam","year":"2024","unstructured":"Aslam MM, Tufail A, De Silva LC, Haji Mohd Apong RAA, Namoun A (2024) An improved autoencoder-based approach for anomaly detection in industrial control systems. Syst Sci Control Eng 12(1):2334303","journal-title":"Syst Sci Control Eng"},{"key":"3163_CR18","doi-asserted-by":"crossref","unstructured":"F\u00e4hrmann D, Mart\u00edn L, S\u00e1nchez L, Damer N (2024) Anomaly detection in smart environments: a comprehensive survey. IEEE access","DOI":"10.1109\/ACCESS.2024.3395051"},{"key":"3163_CR19","doi-asserted-by":"crossref","unstructured":"Kong X, Wang J, Hu Z, He Y, Zhao X, Shen G (2024) Mobile trajectory anomaly detection: Taxonomy, methodology, challenges, and directions. IEEE Internet Things J","DOI":"10.1109\/JIOT.2024.3376457"},{"key":"3163_CR20","doi-asserted-by":"publisher","first-page":"19250","DOI":"10.1109\/ACCESS.2024.3361829","volume":"12","author":"S Baccari","year":"2024","unstructured":"Baccari S, Hadded M, Ghazzai H, Touati H, Elhadef M (2024) Anomaly detection in connected and autonomous vehicles: a survey, analysis, and research challenges. IEEE Access 12:19250\u201319276","journal-title":"IEEE Access"},{"key":"3163_CR21","doi-asserted-by":"publisher","first-page":"3768","DOI":"10.1109\/ACCESS.2023.3349132","volume":"12","author":"P Yan","year":"2024","unstructured":"Yan P, Abdulkadir A, Luley P-P, Rosenthal M, Schatte GA, Grewe BF, Stadelmann T (2024) A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: methods, applications, and directions. IEEE Access 12:3768\u20133789","journal-title":"IEEE Access"},{"issue":"1","key":"3163_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3691338","volume":"57","author":"Z Zamanzadeh Darban","year":"2024","unstructured":"Zamanzadeh Darban Z, Webb GI, Pan S, Aggarwal C, Salehi M (2024) Deep learning for time series anomaly detection: a survey. ACM Comput Surv 57(1):1\u201342","journal-title":"ACM Comput Surv"},{"key":"3163_CR23","unstructured":"Szegedy C (2013) Intriguing properties of neural networks.(arXiv preprint)"},{"key":"3163_CR24","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. (arXiv preprint)"},{"key":"3163_CR25","unstructured":"Madry A (2017) Towards deep learning models resistant to adversarial attacks. (arXiv preprint)"},{"key":"3163_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.104175","volume":"148","author":"H Lu","year":"2025","unstructured":"Lu H, Liu J, Peng J, Lu J (2025) Adversarial attacks based on time-series features for traffic detection. Comput Secur 148:104175","journal-title":"Comput Secur"},{"key":"3163_CR27","doi-asserted-by":"crossref","unstructured":"Khan SU, Mynuddin M, Nabil M (2024) Adaptedge: Targeted universal adversarial attacks on time series data in smart grids. IEEE Trans Smart Grid","DOI":"10.36227\/techrxiv.23542215.v1"},{"issue":"6","key":"3163_CR28","doi-asserted-by":"publisher","first-page":"5103","DOI":"10.1109\/JIOT.2020.2975654","volume":"7","author":"J Li","year":"2020","unstructured":"Li J, Liu Y, Chen T, Xiao Z, Li Z, Wang J (2020) Adversarial attacks and defenses on cyber-physical systems: a survey. IEEE Internet Things J 7(6):5103\u20135115","journal-title":"IEEE Internet Things J"},{"issue":"7","key":"3163_CR29","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","volume":"7","author":"AA Cook","year":"2019","unstructured":"Cook AA, M\u0131s\u0131rl\u0131 G, Fan Z (2019) Anomaly detection for IoT time-series data: a survey. IEEE Internet Things J 7(7):6481\u20136494","journal-title":"IEEE Internet Things J"},{"key":"3163_CR30","unstructured":"Tram\u00e8r F, Zhang F, Juels A, Reiter MK, Ristenpart T (2016) Stealing machine learning models via prediction $$\\{$$APIs$$\\}$$. In: 25th USENIX security symposium (USENIX Security 16), pp 601\u2013618"},{"key":"3163_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102762","volume":"119","author":"Y Qiao","year":"2022","unstructured":"Qiao Y, Zhang W, Tian Z, Yang LT, Liu Y, Alazab M (2022) Adversarial malware sample generation method based on the prototype of deep learning detector. Comput Secur 119:102762","journal-title":"Comput Secur"},{"key":"3163_CR32","doi-asserted-by":"crossref","unstructured":"Papernot N, McDaniel P, Wu X, Jha S, Swami A (2016) Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE symposium on security and privacy (SP), pp 582\u2013597. IEEE","DOI":"10.1109\/SP.2016.41"},{"key":"3163_CR33","doi-asserted-by":"crossref","unstructured":"Mathur AP, Tippenhauer NO (2016) 2016 International workshop on cyber-physical systems for smart water networks (CySWater). IEEE, pp 31\u201336 (Swat: A water treatment testbed for research and training on ics security)","DOI":"10.1109\/CySWater.2016.7469060"},{"key":"3163_CR34","doi-asserted-by":"crossref","unstructured":"Abisheg$$^1$$ S, Raman MG (2024) Adaptive data-driven lstm model. In: Proceedings of applications and techniques in information security: 14th international conference, ATIS 2024, Tamil Nadu, India, November 22\u201324, 2024, 2306, 218. Springer Nature","DOI":"10.1007\/978-981-97-9743-1_16"},{"key":"3163_CR35","doi-asserted-by":"crossref","unstructured":"Sugumar G, Mathur A (2017) Testing the effectiveness of attack detection mechanisms in industrial control systems. In: 2017 IEEE international conference on software quality, reliability and security companion (qrs-c). IEEE, pp 138\u2013145","DOI":"10.1109\/QRS-C.2017.29"},{"key":"3163_CR36","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","volume":"108","author":"S-Y Shih","year":"2019","unstructured":"Shih S-Y, Sun F-K, Lee H (2019) Temporal pattern attention for multivariate time series forecasting. Mach Learn 108:1421\u20131441","journal-title":"Mach Learn"},{"key":"3163_CR37","doi-asserted-by":"crossref","unstructured":"Lai G, Chang W-C, Yang Y, Liu H (2018) Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 95\u2013104","DOI":"10.1145\/3209978.3210006"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03163-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03163-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03163-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:47:17Z","timestamp":1785502037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03163-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":37,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["3163"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03163-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]},"assertion":[{"value":"2 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"334"}}