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Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of(More)
There is significant interest in the network management and industrial security community about the need to identify the ‘‘best’’ and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps(More)
Supervisory Control and Data Acquisition (SCADA) systems monitor and control infrastructures and industrial processes such as smart grid power and water distribution systems. Recently, such systems have been attacked, and traditional security solutions have failed to provide an appropriate level of protection. Therefore, it is important to develop security(More)
Supervisory Control and Data Acquisition (SCADA) systems are a salient part of the control and monitoring of critical infrastructures such as electricity generation, distribution, water treatment and distribution, and gas and oil production. Recently, such systems have increased their connectivity by using public networks and standard protocols (e.g.(More)
Supervisory control and data acquisition (SCADA) systems have become a salient part in controlling critical infrastructures, such as power plants, energy grids, and water distribution systems. In the past decades, these systems were isolated and use proprietary software, operating systems, and protocols. In recent years, SCADA systems have been interfaced(More)