# Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
## 1 Introduction  
With the rapid digitization of energy infrastructure, cybersecurity has become a critical concern for smart grids [1]. Due to their interconnected nature, the use of advanced communication and information technologies to enhance efficiency, reliability, and sustainability renders smart grids increasingly vulnerable to cyber threats. Smart grids, envisioned as the future of power grids, integrate advanced technologies such as sensors, automation, and communication systems for real-time monitoring and control [2, 3]. They facilitate quicker problem identification and support demand response programs, renewable energy integration, and operation in diverse environments with adaptable technologies like Industrial Internet of Things (IIoT), cloud computing, and Software-Defined Networking (SDN). Components such as sensors, meters, controllers, and communication networks provide numerous entry points for potential attackers, leading to severe consequences such as power disruptions and the compromise of critical infrastructure. Ensuring the integrity and resilience of smart grid systems necessitates innovative cybersecurity measures tailored to the evolving threat landscape [4]. Nevertheless, the growing dependence on vulnerable advanced communication systems and highly connected devices has heightened smart grids' vulnerability to cyberattacks and malicious activities [5]. As a result, cyber attackers have a broadened attack surface within the smart grid ecosystem, targeting insecure communication networks, outdated software, weak authentication, and other vulnerabilities [3; 5]. Indeed, a successful cyberattack on a smart grid can cause major disruptions to power generation and distribution, endangering public safety via widespread outages. Moreover, such attacks can compromise sensitive information's confidentiality, integrity, and availability, including customer data, financial records, and critical infrastructure data [6]. Various types of cyberattacks can be launched against smart grids, including Man-in-the-middle (MitM), False data injection, DoS/DDoS (Denial of Service/Distributed Denial of Service), Insider attacks, Phishing attacks, Malware attacks, and physically damaging or sabotaging smart grid components. For example, significant cyberattacks occurred in recent years, targeting Iran's nuclear centrifuges (2010), the Ukrainian power system (2015), the US power grid (2019), Energie de Portugal (2020), and a Ukrainian energy company (2022) [5; 7]. Ensuring cybersecurity is essential for the successful deployment and operation of smart grids. This includes implementing security protocols, deploying intrusion detection and prevention systems, training personnel on cybersecurity best practices, and continuously monitoring and updating the smart grid systems. Consequently, machine learning (ML) has emerged as a popular method to detect and prevent cyberattacks in smart grids [8]. ML algorithms analyze vast amounts of operational data and identify unusual behaviors in network traffic, enabling them to detect potential security threats and alert the system administrators. As a result, ML plays a crucial role in maintaining the security and reliability of smart grid systems. Various ML-based approaches, including supervised, unsupervised, and semi-supervised learning, have been researched to defend against different types of cyberattacks. For example, [PERSON] et al. [9] devised a detection approach using a deep-Q-network (DQN) to safeguard AC power systems against data integrity attacks (DIA). Their study showed that the DQN-based detection approach surpassed the standard methods in regard to accuracy and speed. [PERSON] et al. [10] examined three distinct algorithms--Artificial Neural Networks (ANNs), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM)--incorporating various feature selection (FS) techniques, particularly the genetic algorithm (GA), which proved to be the most efficient FS method. [PERSON] et al.