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

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Critical energy infrastructure (CEI) is specific engineering information about proposed or existing critical infrastructure. Modern critical infrastructures are increasingly turning into distributed, complex cyber-physical systems that need proactive protection and… Click to show full abstract

Critical energy infrastructure (CEI) is specific engineering information about proposed or existing critical infrastructure. Modern critical infrastructures are increasingly turning into distributed, complex cyber-physical systems that need proactive protection and fast restoration to mitigate physical or cyber incidents or attacks. Most importantly, combined cyber-physical attacks are much more challenging and are expected to become the most intrusive attack. This is particularly true for the CEIs. During 2015, the Industrial Control Systems Cyber Emergency Response Team in the Unites States responded to more than 245 incidents; the energy sector tops the list with 32% incidents. Considering the importance of energy in our daily lives and its influence on other critical infrastructures, CEI requires significant attention comparatively. For example, the wind-turbine system is considered one of the most complex cyber-physical infrastructures, causing huge cascading effects to other CEIs, such as electrical power and energy systems and transportation, healthcare sector, communications, industry, and finance. Wind turbines are mainly composed of condition monitoring and operational data (i.e., supervisory command and data acquisition), including air temperature, air pressure, voltage, and power with multiple parameters and periodic characteristics.

Keywords: critical energy; guest editorial; energy; privacy preserving; editorial privacy; cyber physical

Journal Title: IEEE Transactions on Industrial Informatics
Year Published: 2022

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