electric vehicle charging stations. It allows for the remote monitoring and management of the charging process, as well as the collection of usage data. ISO 15118 is a set of international standards for communication between electric vehicles and charging stations. It defines the protocols and procedures for secure and efficient charging and payment transactions. Likewise, ISO/SAE 21434 is a standard for the cybersecurity of road vehicles. It provides guidelines for the design and development of secure systems, networks, and components in vehicles, including electric vehicles and charging stations. It is intended to help protect vehicles against cyber-attacks and ensure the safety and security of passengers and other road users. The IoT ecosystem enriches the EVCS charging station with data. The IoT converts the dummy EVCS stations into an intelligent system that can facilitate the usage and control of these EVCS. This is achieved by enriching the EVCS with data through the IoT. This allows software developers to offer remote monitoring and user accounting to end users. The users can remotely schedule EV charging based on night-time electricity costs. However, this comes with extra challenges. The EVCS equipped with IoT technology became a target for the attackers. There are different techniques in the IT world to detect malicious traffic, such as the Intrusion detection system. However, sophisticated attackers' techniques for staying undetected by IDS, while sending malicious messages through the IoT system, make it difficult to distinguish between malicious and legitimate traffic. IoT network security trumps IT network security. The continual connection needed to serve clients and the large amount of data sent between nodes are the main causes [4]. There are continuous efforts to enhance the effectiveness and accuracy of Intrusion Detection Systems (IDSs), which monitor network traffic to identify potentially harmful data exchanges in IT and IoT environments. To accurately assess IDS systems, it is necessary to use appropriate data [5]. Machine Learning (ML) and Deep Learning (DL) algorithms speed up the development of IDSs that can identify cyberattacks more quickly and accurately [6] making intrusion detection a prominent issue in academics. Costs associated with data breaches in the energy industry have skyrocketed, according to recent research [7]. There is a clear gap in the research for securing the EVCS systems. That is why in this research we focus on the proposed methods to secure the EVCS ecosystem using a  
Figure 1: EVCS components forming the EVCS Ecosystem. machine-learning-based Intrusion detection system to be used in anomaly detection with high accuracy and low false-positive rates. There are little to no proposals to secure the EVCS ecosystem. This paper defines the EV charging environment, EVCS, communication and transport protocol, and EVCSMS management system. Each component's biggest dangers, in addition to attack vectors and ecosystem weaknesses, are presented. This includes charging station, user, and power grid attacks. In the context of this research, we deal with the IoT component of the EVCS. Therefore, throughout this paper, the terms EVCS and IoT are used interchangeably to denote the IoT component of the EVCS. We apply different Machine learning classifying algorithms with an IoT dataset to examine the IDS response and accuracy in the EVCS ecosystem. We also compare two dominant classifying techniques in defending EVCSs against DDoS attacks. The following are the paper's contributions:  
1. Using a native IoT dataset and different Machine Learning classifying techniques to discover fraudulent traffic in EVCSs. 2. Identifying malicious traffic using a limited number of training data, which increases the agility of the system. The remaining sections are structured, as follows: Section 2 discusses the background of the EVCS and the associated attacks. In Section 3, we present the work performed in the literature to secure IoT systems with ML and DL-based IDSs.