## 2 Related Works  
The security of OT protocols has been extensively studied through various methods such as formal verification, simulation, and physical testbeds [9; 10]. As OT equipment in the field becomes increasingly interconnected with accessible IT data networks, it presents greater opportunities for improved maintenance, safety, and operability. However, these advancements also introduce new security and safety risks, as connected devices can potentially become entry points for malicious actors into a previously well-isolated OT system. The growing integration of OT systems with IT networks expands the attack surface and raises significant challenges in securing these systems. A comprehensive review by the authors in [11] examines attacks on DNP3 and other OT protocols over the past 15 years, shedding light on various vulnerabilities that attackers have exploited. Their review includes attacks targeting OT IP filtering systems and emphasizes the need for defense-in-depth strategies, protocol hardening, encryption, and anomaly detection mechanisms to prevent such attacks. In a more specific case, the authors of [12] proposes an Artificial Neural Network (ANN) to detect reconnaissance attacks on the DNP3 protocol. While the approach shows promise, the authors note limitations, particularly the model's inability to generalize, resulting from the limited size and diversity of the training and test datasets. This issue is common in OT security research, where available datasets are often insufficient to model the full range of attack scenarios. In [13], the authors take a different approach, by training a deep neural network to serve as an intrusion detection system (IDS) specifically for detecting attacks against DNP3. Their system demonstrated an impressive 99% accuracy in identifying attacks, showcasing the potential of machine learning to enhance OT security. Building on this work, [PERSON] et al. [14] compared the efficacy of various intrusion detection models, including decision trees, deep neural networks, and transformer neural networks. Their results showed that transformer neural networks achieved the highest accuracy, with a detection rate of 99.56%, which underscores the potential for deep learning models to improve the robustness of OT security systems. The integration of serial OT protocols with modern communication networks, such as the internet, is another critical area of concern. These communication interfaces provide greater connectivity but also introduce new risks related to interoperability and security. The work in [15] presents an interworking model between Modbus and IoT devices, validated in a scenario involving solar energy equipment. This research highlights the complexities of integrating legacy OT protocols with emerging technologies, where maintaining both security and seamless functionality is often a delicate balance. Similarly, in [16], the authors assess the performance of Modbus and DNP3 protocols through both simulation and testbed environments. Their findings indicate that wireless technologies like WiFi, while widely used, are not suitable for meeting the strict latency and security requirements of OT systems. In [17], a security enhancement for the Modbus/TCP protocol is proposed through the use of Chaskey-12 Message Authentication Codes (MACs), as defined in IEC 29192-6 [18]. This cryptographic technique provides an additional layer of security by ensuring the integrity and authenticity of Modbus messages, helping to prevent unauthorized access or manipulation of data during communication. Another important consideration in OT security is the scalability of communication networks, particularly in large-scale deployments. In [19], the authors demonstrate that, due to the sequential polling method of the Modbus protocol, reliable communication becomes infeasible in environments with higher Bit Error Rates (BERs), where retransmissions are required, even before reaching the maximum number of servers supported by the protocol.