## 1 Introduction
On the other hand, with regard to WDSs, which are closely related to public health and environment, few attempts have been made to solve the problem of detecting cyber-attacks on water network systems. [PERSON] et al. [25] proposed a hydrodynamic model for the detection of cyber-attacks and the isolation of the unpressured roof network in an irrigation field. [PERSON] and [PERSON] [26] developed a model for simulating attacks on WDSs considering hydraulic balance and energy consumption. Their proposed model assessed the vulnerability of the systems under cyber-attack conditions by applying an attack situation (e.g., abnormal condition), such as removing one pipe. However, their suggested model could not consider various attack scenarios. With improvements in the handling technology of big-data, studies have then focused on the clustering and classification of such data using artificial intelligence (AI) techniques, such as machine learning. [PERSON] et al. [27] introduced the cyber-attack detection approach based on a data-driven clustering technique in the SCADA system of a simple WDS model. [PERSON] et al. [28] developed the EPANET-CPA (EPANET based Cyber physical attack) based on hydraulic engine (i.e., EPANET 2.0 [29]), which can simulate various cyber-attack scenarios for hydraulic and water quality failures, particularly focusing on CPS components (e.g., sensors, PLCs, and SCADAs). [PERSON] and [PERSON] [30] studied the detection of cyber-attacks using deep-learning techniques; however, their model focused on simulating and characterizing cyber-attacks rather than their detection and identification. The aforementioned studies demonstrate that there is a recent trend in the use of machine learning to accurately detect cyber-attacks in the WDS field. However, since the detection performance of cyber-attack differs depending on the kinds of machine learning algorithms used, a study that determines the most suitable machine learning approach for the detection of cyber-attacks through the quantitative comparison of the performances of various machine learning algorithms will be needed. In summary of the above statements, in the past, the CPSs have been used in several infrastructure systems such as power grid systems and telecommunications systems. However, recent WDSs fields have also used CPSs. For this reason, many researchers developed the operation and management approach of CPSs and cyber-attack detection and prevention approaches, because it can be easy to access in a cyber network. Therefore, this study proposes a novel detection model coupled with machine learning (ML) approaches for the effective detection of cyber-attacks on WDSs. For this reason, this study applied and compared several types of machine learning approaches (i.e., extreme learning machine (ELM), k-nearest neighbor (KNN) algorithm, Support Vector Machine (SVM), and artificial neural networks (ANNs)) for finding the best ML algorithm. This study not only searches for the best performing algorithm through simple algorithm comparison but also considers the improved version of the basic algorithm to show the best performance. Therefore, this study could become a foundational study in the future development of cyber-attack detection techniques in WDSs fields. Furthermore, this study used a dataset from the Battle of the Attack Detection Algorithms (BATADAL [31]) for the various cyber-attack scenarios and a comparison was performed using various representative performance indices which measure the prediction accuracy quantitatively and select the most appropriate algorithm.