A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning
Ines Ortega-Fernandez, Francesco Liberati
Abstract
The smart grid merges cyber-physical systems (CPS) infrastructure with information and communication technologies (ICT) to ensure efficient power generation, smart energy distribution in real-time, and optimisation, and it is rapidly becoming the current standard for energy generation and distribution. However, the use of ICT has increased the attack surface against the electricity grid, which is vulnerable to a wider range of cyberattacks. In particular, Denial-of-Service (DoS) attacks might impact both the communication network and the cyber-physical layer. DoS attacks have become critical threats against the smart grid due to their ability to impact the normal operation of legitimate smart-grid devices and their ability to target different smart grid systems and applications. This paper presents a comprehensive and methodical discussion of DoS attacks in the smart grid, analysing the most common attack vectors and their effect on the smart grid. The paper also presents a survey of detection and mitigation techniques against DoS attacks in the smart grid using reinforcement learning (RL) algorithms, analysing the strengths and limitations of the current approaches and identifying the prospects for future research.