{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T23:08:50Z","timestamp":1768259330791,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03613-7","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T14:19:42Z","timestamp":1736259582000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["HGCNN-LSTM: A Data-driven Approach for Cyberattack Detection in Cyber-Physical Systems"],"prefix":"10.1007","volume":"6","author":[{"given":"S.","family":"Abinash","sequence":"first","affiliation":[]},{"given":"N.","family":"Srivatsan","sequence":"additional","affiliation":[]},{"given":"S. K.","family":"Hemachandran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0233-3186","authenticated-orcid":false,"given":"S.","family":"Priyanga","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"3613_CR1","doi-asserted-by":"publisher","first-page":"103201","DOI":"10.1016\/j.micpro.2020.103201","volume":"77","author":"J-P Yaacoub","year":"2020","unstructured":"Yaacoub J-P, Salman O, Noura HN, Kaaniche N, Chehab A, Malli M. Cyber-physical systems security: Limitations, issues and future trends. Microprocess Microsyst. 2020;77:103201.","journal-title":"Microprocess Microsyst"},{"issue":"9","key":"3613_CR2","doi-asserted-by":"publisher","first-page":"3751","DOI":"10.3390\/app11093751","volume":"11","author":"Z Wang","year":"2021","unstructured":"Wang Z, Xie W, Wang B, Tao J, Wang E. A survey on recent advanced research of CPS security. Appl Sci. 2021;11(9):3751.","journal-title":"Appl Sci"},{"issue":"3","key":"3613_CR3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/MSP.2011.67","volume":"9","author":"R Langner","year":"2011","unstructured":"Langner R. Stuxnet: Dissecting a cyberwarfare weapon. IEEE Secur Priv. 2011;9(3):49\u201351.","journal-title":"IEEE Secur Priv"},{"issue":"11","key":"3613_CR4","doi-asserted-by":"publisher","first-page":"7618","DOI":"10.1109\/TII.2021.3053304","volume":"17","author":"K-D Lu","year":"2021","unstructured":"Lu K-D, Zeng G-Q, Luo X, Weng J, Luo W, Yongdong Wu. Evolutionary deep belief network for cyber-attack detection in industrial automation and control system. IEEE Trans Industr Inf. 2021;17(11):7618\u201327.","journal-title":"IEEE Trans Industr Inf"},{"key":"3613_CR5","first-page":"2021","volume":"1","author":"ICS Kaspersky","year":"2021","unstructured":"Kaspersky ICS. Threat landscape for industrial automation systems. Statistics for H. 2021;1:2021.","journal-title":"Statistics for H"},{"key":"3613_CR6","doi-asserted-by":"publisher","first-page":"101666","DOI":"10.1016\/j.cose.2019.101666","volume":"89","author":"D Upadhyay","year":"2020","unstructured":"Upadhyay D, Sampalli S. SCADA (Supervisory Control and Data Acquisition) systems: Vulnerability assessment and security recommendations. Comput Secur. 2020;89:101666.","journal-title":"Comput Secur"},{"key":"3613_CR7","doi-asserted-by":"publisher","first-page":"106946","DOI":"10.1016\/j.comnet.2019.106946","volume":"165","author":"MR Asghar","year":"2019","unstructured":"Asghar MR, Qinwen Hu, Zeadally S. Cybersecurity in industrial control systems: Issues, technologies, and challenges. Comput Net. 2019;165:106946.","journal-title":"Comput Net"},{"key":"3613_CR8","doi-asserted-by":"crossref","unstructured":"Xu, Yikai, Yi Yang, Tianran Li, Jiaqi Ju, and Qi Wang. \"Review on cyber vulnerabilities of communication protocols in industrial control systems.\" In\u00a02017 IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1\u20136. IEEE, 2017.","DOI":"10.1109\/EI2.2017.8245509"},{"issue":"1","key":"3613_CR9","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1109\/COMST.2018.2847722","volume":"21","author":"P Mishra","year":"2018","unstructured":"Mishra P, Varadharajan V, Tupakula U, Pilli ES. A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun Surv Tutorials. 2018;21(1):686\u2013728.","journal-title":"IEEE Commun Surv Tutorials"},{"key":"3613_CR10","doi-asserted-by":"publisher","first-page":"3255","DOI":"10.1007\/s10462-019-09762-z","volume":"53","author":"MR Gauthama Raman","year":"2020","unstructured":"Gauthama Raman MR, Somu N, Jagarapu S, Manghnani T, Selvam T, Krithivasan K, Shankar Sriram VS. An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm. Art Intell Rev. 2020;53:3255\u201386.","journal-title":"Art Intell Rev"},{"key":"3613_CR11","doi-asserted-by":"publisher","first-page":"106771","DOI":"10.1016\/j.engappai.2023.106771","volume":"126","author":"G Zhang","year":"2023","unstructured":"Zhang G, Li J, Bamisile O, Xing Y, Cao Di, Huang Qi. Identification and classification for multiple cyber attacks in power grids based on the deep capsule CNN. Eng Appl Artif Intell. 2023;126:106771.","journal-title":"Eng Appl Artif Intell"},{"key":"3613_CR12","doi-asserted-by":"publisher","first-page":"100582","DOI":"10.1016\/j.ijcip.2022.100582","volume":"40","author":"K Bitirgen","year":"2023","unstructured":"Bitirgen K, Filik \u00dcB. A hybrid deep learning model for discrimination of physical disturbance and cyber-attack detection in smart grid. Int J Critical Infrastruct Prot. 2023;40:100582.","journal-title":"Int J Critical Infrastruct Prot."},{"key":"3613_CR13","doi-asserted-by":"publisher","first-page":"102585","DOI":"10.1016\/j.cose.2021.102585","volume":"114","author":"D Nedeljkovic","year":"2022","unstructured":"Nedeljkovic D, Jakovljevic Z. CNN based method for the development of cyber-attacks detection algorithms in industrial control systems. Comput Secur. 2022;114:102585.","journal-title":"Comput Secur"},{"key":"3613_CR14","doi-asserted-by":"publisher","first-page":"103570","DOI":"10.1016\/j.cose.2023.103570","volume":"139","author":"H Sun","year":"2024","unstructured":"Sun H, Huang Y, Han L, Cai Fu, Liu H, Long X. MTS-DVGAN: Anomaly detection in cyber-physical systems using a dual variational generative adversarial network. Comput Secur. 2024;139:103570.","journal-title":"Comput Secur"},{"key":"3613_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcip.2024.100676","author":"F Zare","year":"2024","unstructured":"Zare F, Mahmoudi-Nasr P, Yousefpour R. A real-time network-based anomaly detection in industrial control systems. Int J Crit Infrastruct Protect. 2024. https:\/\/doi.org\/10.1016\/j.ijcip.2024.100676.","journal-title":"Int J Crit Infrastruct Protect"},{"key":"3613_CR16","doi-asserted-by":"publisher","first-page":"120562","DOI":"10.1016\/j.ins.2024.120562","volume":"670","author":"Z Zhang","year":"2024","unstructured":"Zhang Z, Li M, Xie L. Data-driven replay attack detection for unknown cyber-physical systems. Inf Sci. 2024;670:120562.","journal-title":"Inf Sci"},{"key":"3613_CR17","doi-asserted-by":"publisher","first-page":"4394","DOI":"10.1109\/TIA.2020.2977872","volume":"56","author":"S Priyanga","year":"2020","unstructured":"Priyanga S, Krithivasan K, Pravinraj S, Shankar Sriram VS. Detection of cyberattacks in industrial control systems using enhanced principal component analysis and hypergraph-based convolution neural network (EPCA-HG-CNN). IEEE Transact Indust Appl. 2020;56:4394\u2013404.","journal-title":"IEEE Transact Indust Appl"},{"key":"3613_CR18","first-page":"323","volume-title":"International Conference on Computational Intelligence in Pattern Recognition","author":"S Priyanga","year":"2022","unstructured":"Priyanga S, Pravinraj S, Repalle VB, Krithivasan K, Shankar Sriram VS. Detection of Cyberattacks in Cyber-Physical Systems Using Supervised Learning and Hypergraphs. In: International Conference on Computational Intelligence in Pattern Recognition. Singapore: Springer Nature Singapore; 2022. p. 323\u201336."},{"key":"3613_CR19","doi-asserted-by":"publisher","first-page":"3993","DOI":"10.3233\/JIFS-169960","volume":"36","author":"S Priyanga","year":"2019","unstructured":"Priyanga S, Gauthama Raman MR, Jagtap SS, Aswin N, Kirthivasan K, Shankar Sriram VS. An improved rough set theory based feature selection approach for intrusion detection in SCADA systems. J Intell Fuzzy Syst. 2019;36:3993\u20134003.","journal-title":"J Intell Fuzzy Syst"},{"issue":"11","key":"3613_CR20","doi-asserted-by":"publisher","first-page":"7704","DOI":"10.1109\/TII.2020.3025755","volume":"17","author":"M Abdel-Basset","year":"2020","unstructured":"Abdel-Basset M, Chang V, Hawash H, Chakrabortty RK, Ryan M. Deep-IFS: Intrusion detection approach for industrial internet of things traffic in fog environment. IEEE Trans Industr Inf. 2020;17(11):7704\u201315.","journal-title":"IEEE Trans Industr Inf"},{"key":"3613_CR21","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.future.2020.03.042","volume":"110","author":"N Koroniotis","year":"2020","unstructured":"Koroniotis N, Moustafa N, Sitnikova E. A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework. Futur Gener Comput Syst. 2020;110:91\u2013106.","journal-title":"Futur Gener Comput Syst"},{"issue":"8","key":"3613_CR22","doi-asserted-by":"publisher","first-page":"151","DOI":"10.3390\/computers12080151","volume":"12","author":"M Krichen","year":"2023","unstructured":"Krichen M. Convolutional neural networks: A survey. Computers. 2023;12(8):151.","journal-title":"Computers"},{"key":"3613_CR23","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"V Houdt","year":"2020","unstructured":"Houdt V, Greg CM, N\u00e1poles G. A review on the long short-term memory model. Art Intell Rev. 2020;53:5929\u201355.","journal-title":"Art Intell Rev"},{"key":"3613_CR24","unstructured":"Berge, Claude. \"Graphs and hypergraphs.\" (1973)."},{"key":"3613_CR25","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-981-99-0185-2_2","volume-title":"Hypergraph Computation","author":"Q Dai","year":"2023","unstructured":"Dai Q, Gao Y. Mathematical Foundations of Hypergraph. In: Hypergraph Computation. Singapore: Springer Nature Singapore; 2023. p. 19\u201340."},{"issue":"6","key":"3613_CR26","doi-asserted-by":"publisher","first-page":"2771","DOI":"10.1109\/TKDE.2020.3017120","volume":"34","author":"J Sybrandt","year":"2020","unstructured":"Sybrandt J, Shaydulin R, Safro I. Hypergraph partitioning with embeddings. IEEE Trans Knowl Data Eng. 2020;34(6):2771\u201382.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3613_CR27","first-page":"88","volume":"10242","author":"J Goh","year":"2017","unstructured":"Goh J, Adepu S, Junejo KN, Mathur A. \u201cA dataset to support research in the design of secure water treatment systems\u201d, Proc. 11th Int. Conf Crit Inf Infrastructures Secur. 2017;10242:88\u201399.","journal-title":"Conf. Crit. Inf. Infrastructures Secur."},{"key":"3613_CR28","first-page":"1","volume":"2020","author":"C Wang","year":"2020","unstructured":"Wang C, Wang B, Liu H, Haikuo Qu. Anomaly detection for industrial control system based on autoencoder neural network. Wirel Commun Mob Comput. 2020;2020:1\u201310.","journal-title":"Wirel Commun Mob Comput"},{"issue":"6","key":"3613_CR29","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1016\/j.ifacol.2022.07.204","volume":"55","author":"M Al-Dhaheri","year":"2022","unstructured":"Al-Dhaheri M, Zhang P, Mikhaylenko D. Detection of cyber attacks on a water treatment process. IFAC-Papers Online. 2022;55(6):667\u201372.","journal-title":"IFAC-Papers Online"},{"key":"3613_CR30","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1016\/j.ins.2023.01.136","volume":"629","author":"W Wu","year":"2023","unstructured":"Wu W, Song C, Zhao J, Zuhua Xu. Physics-informed gated recurrent graph attention unit network for anomaly detection in industrial cyber-physical systems. Inf Sci. 2023;629:618\u201333.","journal-title":"Inf Sci"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03613-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03613-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03613-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T16:10:50Z","timestamp":1736266250000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03613-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,7]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["3613"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03613-7","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,7]]},"assertion":[{"value":"30 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}],"article-number":"69"}}