## 1 Introduction
#### Relevant Literature  
Three main types of cyber-attacks are discussed in this article. A hijacking attack takes control of a system or communication process; a false data injection (FDI) attack manipulates data to mislead the system into making incorrect decisions; and a denial of service (DoS) attack overwhelms resources, resulting in disruptions to availability and functionality. In particular, FDI attacks [5] can disrupt the balance between production and consumption, while DoS attacks [6] can prevent the accessibility of the system. Such attacks can jeopardize energy supply security, lead to operational losses, and reduce critical infrastructure reliability. Strong cybersecurity measures, regular monitoring, intelligent transportation systems for traffic congestion [7], and secure communication protocols are vital solutions for mitigating these vulnerabilities. Similarly, a cyber-physical system, such as a healthcare system, has been reported to further enhance cyber threat detection schemes [8] using machine learning (ML) techniques. Therefore, the application of intelligent methodologies has been becoming prominent in microgrids [9], inverter-based systems with anomaly identification [10], detection of cyber-attacks on voltage regulation [11], a rooftop photovoltaic (PV) production system for cyber-attack detection [12], artificial neural network (ANN)-based cyber-attack detection for electric vehicles [13] and using artificial intelligence (AI)-coupled cybersecurity system for a diverse range of PV systems [14] of cyber-physical systems. In addition to the aforementioned methods, ANN-based tools have been widely used for detection, classification, and prediction with different learning schemes [15]. Some current approaches, such as transfer learning for motor drives [16], reinforcement learning in smart grids [17], cyber-attack defense with high penetration of DERs [18], and energy storage systems [19], have been proposed recently. The standard utilization of information and communication technology in RESs to achieve carbon emission and green energy targets exposes electrical infrastructure to manipulate cyber-attacks [20]. These cyber-threats can pose a danger by attacking the control layer [21], sensor measurement units [22], communication links [23], or any other places that are connected to the net. Information technologies should be strictly secured to defend electrical power systems. The cyber-physical system includes information technologies, which are inevitable for the up-to-date state of the art. In this context, cyber-attacks, i.e., threats from adversaries, should be adequately detected to eliminate vulnerability and visualize how cyber-attacks on power grids may lead to cascading failures [24]. As mentioned, two distinct types of cyber-attacks (DoSs and FDIs) pose substantial threats; however, they require different detection and mitigation strategies. While DoS attacks focus on servers and networks, FDI attacks target control systems and sensors [14]. For a better illustration, DoS attacks can be considered quite visible, but FDIs are stealthy. FDIs can manipulate data to make the target system behave incorrectly. The main aim of DoS attacks is to disrupt service as it is unavailable [25]. In 2015, a global FDI-based cyber-attack on the electricity grid was reported to make system operators overpower [26]. Malicious manipulations of any adversary can render anomalous data for regular operation. In this aspect, anomalous data shows unusual elements in the data stream that deviate from expected behavior patterns. Although errors can cause such anomalies, error-induced anomalies are generally less sophisticated. In contrast, attack-based anomalies can be specifically designed and injected into the system with stealth attacks to create targeted damage. Such anomalous circumstances can be designed to propagate through the communication links into the target system [20] and threaten wireless access environments via security and privacy attacks [27].