Guest Editorial: Privacy-Preserving Federated Machine Learning Solutions for Enhanced Security of Critical Energy Infrastructures

Abstract

Security is paramount in protecting critical infrastructures such as healthcare, power, water distribution, and transport system. Modern critical infrastructures are increasingly turning into distributed, complex Cyber-Physical systems that enable data monitoring in near real-time conditions. With the advancement in distributed IoT systems, the magnitude of the threats has increased. An innovative approach is to adopt Privacy-Preserving machine learning solutions to detect any possible anomalies in such infrastructures. This issue aims to contribute to the body of knowledge on enhancing critical infrastructure security in developing machine learning solutions and benefiting our future fighting against cyber threats in critical infrastructure.