Balta, SedaZavrak, S.Eken, S.2023-07-262023-07-2620229.78303E+121867-8211https://doi.org/10.1007/978-3-031-01984-5_17https://hdl.handle.net/20.500.12684/130501st International Congress of Electrical and Computer Engineering, ICECENG 2022 -- 9 February 2022 through 12 February 2022 -- 277759SCADA networks, which are widely used by governments around the world to run computers and applications that perform a wide range of important functions and provide critical services to their infrastructure, are becoming increasingly popular among organizations. Because of their critical role in the infrastructure, as well as the fact that they are a potential target for cyberattacks, they must be secured and protected in some way at all times. In this study, we propose a topic-based pub/sub messaging system based on Apache Spark and Apache Kafka for real-time monitoring and detection of cyber-physical attacks in SCADA systems, which can be used in conjunction with other currently available systems. There are a variety of traditional machine learning approaches used in conjunction with a deep learning encoded decoder algorithm to create the mechanism for attack detection. The performance results demonstrate that our system outperforms the current state of the art described in the literature in this field. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.en10.1007/978-3-031-01984-5_17info:eu-repo/semantics/closedAccessData streamsIndustrial control systemsIoTPub-sub patternReal-time data processingSCADA networks securityComputer crimeCyber attacksDeep learningEmbedded systemsInternet of thingsIntrusion detectionNetwork securityReal time systemsSCADA systemsWater distribution systemsCyber physicalsData streamIndustrial control systemsNetworks securityPub-sub patternPub/subReal time monitoringReal-time data processingSCADA network securitySub-patternsCyber Physical SystemConference Object436 LNICST2032152-s2.0-85130216539N/A