Artificial intelligence for cybersecurity monitoring of cyber-physical power electronic converters: a DC/DC power converter case study

• Published in: Scientific reports 2024-09, Vol.14 (1), p.22072-26, Article 22072
• Main Authors: Habibi, Mohammad Reza, Guerrero, Josep M., Vasquez, Juan C.
• Format: Article
• Language: English
• Subjects: 639/4077/4073/4071; 639/4077/4073/4099; Artificial Intelligence; Communication; Cyber-physical systems; Cybersecurity; False data injection attacks; Humanities and Social Sciences; Long short-term memory; multidisciplinary; Neural networks; Noise; Power converters; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-72286-2

Power electronic converters are widely implemented in many types of power applications such as microgrids. Power converters can make a physical connection between the power resources and the power application. To control a power converter, required data such as the voltage and the current of that should be measured to be used in a control application. Therefore, a communication-based structure including sensors and communication links can be used to measure the desired data and transmit that to the controllers. So, a power converter-based system can be considered as a type of cyber-physical system, and it can be vulnerable to cyber-attacks. Then, it can strongly be recommended to use a strategy for a power converter-based system to monitor the system and identify the existence of cyber-attack in the system. In this study, artificial intelligence (AI) is deployed to calculate the value of the false data (i.e., constant false data, and time-varying false data) and detect false data injection cyber-attacks on power converters. Besides, to have a precise technical evaluation of the proposed methodology, that is evaluated under other issues, i.e., noise, and communication link delay. In the case of noise, the proposed strategy is examined under noises with different signal-to-noise ratios . Further, for the case of the communication delay, the system is examined under both symmetrical (i.e., same communication delay on all inputs) and unsymmetrical communication delays (i.e., different communication delay/delays on the inputs). In this work, artificial neural networks are implemented as the AI-based application, and two types of the networks, i.e., feedforward (as a basic type) and long short-term memory (LSTM)-based network as a more complex network are tested. Finally, three important AI-based techniques (regression, classification, and clustering) are examined. Based on the obtained results, this work can properly identify and calculate the false data in the system.