{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T17:43:43Z","timestamp":1792950223688,"version":"4.5.0"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008234","name":"Lehigh University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008234","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011753","name":"Lehigh University P C Rossin College of Engineering and Applied Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011753","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Water Research"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1016\/j.watres.2025.123517","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T13:00:36Z","timestamp":1743253236000},"page":"123517","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":13,"special_numbering":"C","title":["Optimal cybersecurity framework for smart water system: Detection, localization and severity assessment"],"prefix":"10.1016","volume":"281","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5433-8720","authenticated-orcid":false,"given":"Nazia","family":"Raza","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3178-2055","authenticated-orcid":false,"given":"Faegheh","family":"Moazeni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.watres.2025.123517_b1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s42979-024-03613-7","article-title":"HGCNN-LSTM: A data-driven approach for cyberattack detection in cyber-physical systems","volume":"6","author":"Abinash","year":"2025","journal-title":"SN Comput. Sci."},{"issue":"1","key":"10.1016\/j.watres.2025.123517_b2","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)WR.1943-5452.0001023","article-title":"Real-time identification of cyber-physical attacks on water distribution systems via machine learning\u2013based anomaly detection techniques","volume":"145","author":"Abokifa","year":"2019","journal-title":"J. Water Resour. Plan. Manag."},{"key":"10.1016\/j.watres.2025.123517_b3","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3384295","article-title":"A CVAE-based anomaly detection algorithm for cyber physical attacks for water distribution systems","author":"Addeen","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.watres.2025.123517_b4","doi-asserted-by":"crossref","first-page":"99905","DOI":"10.1109\/ACCESS.2021.3095713","article-title":"A survey of cyber-physical attacks and detection methods in smart water distribution systems","volume":"9","author":"Addeen","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.watres.2025.123517_b5","series-title":"Outlier Analysis","author":"Aggarwal","year":"2017"},{"key":"10.1016\/j.watres.2025.123517_b6","doi-asserted-by":"crossref","unstructured":"Aghashahi, Mohsen, Sundararajan, Raanju, Pourahmadi, Mohsen, Banks, M Katherine, 2017. Water distribution systems analysis symposium\u2013battle of the attack detection algorithms (batadal). In: World Environmental and Water Resources Congress 2017. pp. 101\u2013108.","DOI":"10.1061\/9780784480595.010"},{"key":"10.1016\/j.watres.2025.123517_b7","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"2623","article-title":"Optuna: A next-generation hyperparameter optimization framework","author":"Akiba","year":"2019"},{"key":"10.1016\/j.watres.2025.123517_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2024.101092","article-title":"An explainable multi-modal model for advanced cyber-attack detection in industrial control systems","volume":"25","author":"Bahadoripour","year":"2024","journal-title":"Internet Things"},{"key":"10.1016\/j.watres.2025.123517_b9","unstructured":"Bergstra, James, Bardenet, Remi, Bengio, Yoshua, Kegl, Balazs, 2011. Algorithms for Hyper-Parameter Optimization. In: Proceedings of the 24th International Conference on Neural Information Processing Systems. NIPS, pp. 2546\u20132554."},{"key":"10.1016\/j.watres.2025.123517_b10","doi-asserted-by":"crossref","unstructured":"Brentan, Bruno M, Campbell, E, Lima, G, Manzi, D, Ayala-Cabrera, David, Herrera, M, Montalvo, I, Izquierdo, Joaquin, Luvizotto, E, 2017. On-line cyber attack detection in water networks through state forecasting and control by pattern recognition. In: World Environmental and Water Resources Congress 2017. pp. 583\u2013592.","DOI":"10.1061\/9780784480625.054"},{"key":"10.1016\/j.watres.2025.123517_b11","doi-asserted-by":"crossref","unstructured":"Chandy, Sarin E, Rasekh, Amin, Barker, Zachary A, Campbell, Bruce, Shafiee, M Ehsan, 2017. Detection of cyber-attacks to water systems through machine-learning-based anomaly detection in SCADA data. In: World Environmental and Water Resources Congress 2017. pp. 611\u2013616.","DOI":"10.1061\/9780784480625.057"},{"key":"10.1016\/j.watres.2025.123517_b12","series-title":"Neural Network Architectures: an Introduction","author":"Dayhoff","year":"1990"},{"key":"10.1016\/j.watres.2025.123517_b13","series-title":"Prepared together \u2013 cyber storm IX recap","author":"Easterly","year":"2024"},{"issue":"55","key":"10.1016\/j.watres.2025.123517_b14","first-page":"1","article-title":"Neural architecture search: A survey","volume":"20","author":"Elsken","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.watres.2025.123517_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2022.118973","article-title":"The role of deep learning in urban water management: A critical review","volume":"223","author":"Fu","year":"2022","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2025.123517_b16","series-title":"European Symposium on Research in Computer Security","first-page":"493","article-title":"Perspectives from a comprehensive evaluation of reconstruction-based anomaly detection in industrial control systems","author":"Fung","year":"2022"},{"key":"10.1016\/j.watres.2025.123517_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2024.122396","article-title":"Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks","volume":"266","author":"Garz\u00f3n","year":"2024","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2025.123517_b18","series-title":"Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow","author":"G\u00e9ron","year":"2022"},{"key":"10.1016\/j.watres.2025.123517_b19","doi-asserted-by":"crossref","unstructured":"Giacomoni, Marcio, Gatsis, Nikolaos, Taha, Ahmad, 2017. Identification of cyber attacks on water distribution systems by unveiling low-dimensionality in the sensory data. In: World Environmental and Water Resources Congress 2017. pp. 660\u2013675.","DOI":"10.1061\/9780784480625.062"},{"key":"10.1016\/j.watres.2025.123517_b20","series-title":"Neural Network Design","author":"Hagan","year":"1997"},{"key":"10.1016\/j.watres.2025.123517_b21","doi-asserted-by":"crossref","unstructured":"He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, Sun, Jian, 2015. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision. ICCV, pp. 1026\u20131034.","DOI":"10.1109\/ICCV.2015.123"},{"issue":"2","key":"10.1016\/j.watres.2025.123517_b22","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1023\/B:AIRE.0000045502.10941.a9","article-title":"A survey of outlier detection methodologies","volume":"22","author":"Hodge","year":"2004","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.watres.2025.123517_b23","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.watres.2018.03.039","article-title":"Model-based approach for cyber-physical attack detection in water distribution systems","volume":"139","author":"Housh","year":"2018","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2025.123517_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2023.120037","article-title":"Development of an embedded molecular structure-based model for prediction of micropollutant treatability in a drinking water treatment plant by machine learning from three years monitoring data","volume":"239","author":"Kang","year":"2023","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2025.123517_b25","series-title":"Water Network Tool for Resilience (WNTR) User Manual: Version 0.2.3","first-page":"82","author":"Klise","year":"2020"},{"key":"10.1016\/j.watres.2025.123517_b26","series-title":"Federal officials investigating after pro-Iran group allegedly hacked water authority in Pennsylvania","author":"Lyngaas","year":"2023"},{"key":"10.1016\/j.watres.2025.123517_b27","series-title":"An introduction to convolutional neural networks","author":"O\u2019shea","year":"2015"},{"key":"10.1016\/j.watres.2025.123517_b28","doi-asserted-by":"crossref","unstructured":"Pasha, M Fayzul K, Kc, Bijay, Somasundaram, Saravanakumar Lakshmanan, 2017. An approach to detect the cyber-physical attack on water distribution system. In: World Environmental and Water Resources Congress 2017. pp. 703\u2013711.","DOI":"10.1061\/9780784480625.065"},{"key":"10.1016\/j.watres.2025.123517_b29","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.watres.2018.02.052","article-title":"Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products","volume":"136","author":"Peleato","year":"2018","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2025.123517_b30","series-title":"Real-Time Monitoring and Operational Control of Drinking-Water Systems","author":"Puig","year":"2017"},{"key":"10.1016\/j.watres.2025.123517_b31","doi-asserted-by":"crossref","first-page":"190403","DOI":"10.1109\/ACCESS.2020.3032251","article-title":"Behavioural intrusion detection in water distribution systems using neural networks","volume":"8","author":"Ramotsoela","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.watres.2025.123517_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijcip.2023.100645","article-title":"Chance-constrained vulnerability assessment of smart water distribution systems against stealthy false data injection attacks","volume":"44","author":"Raza","year":"2024","journal-title":"Int. J. Crit. Infrastruct. Prot."},{"key":"10.1016\/j.watres.2025.123517_b33","series-title":"Progress in Artificial Intelligence and Pattern Recognition: 7th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2021, Havana, Cuba, October 5\u20137, 2021, Proceedings 7","first-page":"79","article-title":"A novel approach for detection and location of cyber-attacks in water distribution networks","author":"Rodr\u00edguez Mart\u00ednez","year":"2021"},{"issue":"8","key":"10.1016\/j.watres.2025.123517_b34","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"10.1016\/j.watres.2025.123517_b35","article-title":"Autonomy in weapon systems","author":"Scharre","year":"2015","journal-title":"Cent. A New Am. Secur. Work. Pap."},{"issue":"2","key":"10.1016\/j.watres.2025.123517_b36","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1061\/(ASCE)0733-9496(2008)134:2(181)","article-title":"Optimal real-time operation of urban water distribution systems using reduced models","volume":"134","author":"Shamir","year":"2008","journal-title":"J. Water Resour. Plan. Manag."},{"key":"10.1016\/j.watres.2025.123517_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.jwpe.2023.103568","article-title":"Deep H2O: Cyber attacks detection in water distribution systems using deep learning","volume":"52","author":"Sikder","year":"2023","journal-title":"J. Water Process. Eng."},{"key":"10.1016\/j.watres.2025.123517_b38","unstructured":"Snoek, Jasper, Larochelle, Hugo, Adams, Ryan P., 2012. Practical Bayesian Optimization of Machine Learning Algorithms. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. NIPS, pp. 2951\u20132959."},{"key":"10.1016\/j.watres.2025.123517_b39","doi-asserted-by":"crossref","unstructured":"Taormina, R., Galelli, S., 2017. Real-time detection of cyber-physical attacks on water distribution systems using deep learning. In: World Environmental and Water Resources Congress 2017. pp. 469\u2013479.","DOI":"10.1061\/9780784480625.043"},{"issue":"5","key":"10.1016\/j.watres.2025.123517_b40","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)WR.1943-5452.0000749","article-title":"Characterizing cyber-physical attacks on water distribution systems","volume":"143","author":"Taormina","year":"2017","journal-title":"J. Water Resour. Plan. Manag."},{"issue":"8","key":"10.1016\/j.watres.2025.123517_b41","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)WR.1943-5452.0000969","article-title":"Battle of the attack detection algorithms: Disclosing cyber attacks on water distribution networks","volume":"144","author":"Taormina","year":"2018","journal-title":"J. Water Resour. Plan. Manag."},{"issue":"9","key":"10.1016\/j.watres.2025.123517_b42","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.3390\/w13091247","article-title":"Cyber\u2014Physical attack detection in water distribution systems with temporal graph convolutional neural networks","volume":"13","author":"Tsiami","year":"2021","journal-title":"Water"},{"issue":"1","key":"10.1016\/j.watres.2025.123517_b43","doi-asserted-by":"crossref","first-page":"81","DOI":"10.3390\/w13010081","article-title":"A systematic review of the state of cyber-security in water systems","volume":"13","author":"Tuptuk","year":"2021","journal-title":"Water"},{"key":"10.1016\/j.watres.2025.123517_b44","doi-asserted-by":"crossref","DOI":"10.1021\/acs.iecr.4c03601","article-title":"Physics-informed graph convolutional recurrent network for cyber-attack detection in chemical process networks","author":"Wu","year":"2025","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.watres.2025.123517_b45","series-title":"Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists","author":"Zheng","year":"2018"},{"key":"10.1016\/j.watres.2025.123517_b46","unstructured":"Zoph, Barret, Le, Quoc V., 2017. Neural Architecture Search with Reinforcement Learning. In: Proceedings of the International Conference on Learning Representations. ICLR."}],"container-title":["Water Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0043135425004300?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0043135425004300?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T08:23:23Z","timestamp":1748507003000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0043135425004300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":46,"alternative-id":["S0043135425004300"],"URL":"https:\/\/doi.org\/10.1016\/j.watres.2025.123517","relation":{},"ISSN":["0043-1354"],"issn-type":[{"value":"0043-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Optimal cybersecurity framework for smart water system: Detection, localization and severity assessment","name":"articletitle","label":"Article Title"},{"value":"Water Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.watres.2025.123517","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"123517"}}