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FedDiSC:A computation-efficient federated learning framework for power systems disturbance and cyber attack discrimination

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Energy and AI 2023年第4期14卷 212-220页

作者： muhammad akbar husnoo Adnan Anwar Haftu Tasew Reda Nasser Hosseinzadeh Shama Naz Islam Abdun Naser Mahmood Robin Doss Centre for Cyber Security Research and Innovation(CSRI) School of Information TechnologyDeakin UniversityGeelongVIC 3216Australia Centre for Smart Power and Energy Research(CSPER) School of EngineeringDeakin UniversityGeelongVIC 3216Australia Department of Computer Science&IT Latrobe UniversityBundooraVIC 3086Australia

With the growing concern about the security and privacy of smart grid systems,cyberattacks on critical power grid components,such as state estimation,have proven to be one of the top-priority cyber-related issues and have received significant attention in recent ***,cyberattack detection in smart grids now faces new challenges,including privacy preservation and decentralized power zones with strategic data *** address these technical bottlenecks,this paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework,known as FedDiSC,that enables Discrimination between power System disturbances and ***,we first propose a Federated Learning approach to enable Supervisory Control and Data Acquisition subsystems of decentralized power grid zones to collaboratively train an attack detection model without sharing sensitive power related ***,we put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity ***,to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs,we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication *** simulations of the proposed framework on publicly available Industrial Control Systems datasets demonstrate that the proposed framework can achieve superior detection accuracy while preserving the privacy of sensitive power grid related ***,we find that the gradient quantization scheme utilized improves communication efficiency by 40%when compared to a traditional federated learning approach without gradient quantization which suggests suitability in a real-world scenario.

关键词： Federated Learning Anomaly detection Cyber attack Internet of things(IoT) Smart grid

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