Towards Robust Misbehavior Detection in Power Control Systems: A Gradient Quantization Approach
Muhammad Akbar Husnoo, Adnan Anwar, Robin Doss, Nasser Hosseinzadeh · 2023
The decentralization of modern power grid systems in this new era of advanced communication and information technology to enhance efficacy and efficiency has created a new class of privacy challenges which requires innovative approaches. In recent times, Federated Learning (FL) has surfaced as a promising privacy-preserving solution to misbehaviour detection in smart grids which enables the collaborative learning of a model without requiring sharing of raw sensitive power-related data. Despite its virtues, recent literature have highlighted that FL-based approaches are inherently prone to Byzantine threats due to their potential in compromising the integrity of the learning process and undermining the performance and reliability of misbehaviour detection models. Therefore, to tackle these technical impediments, this manuscript puts forward a novel privacy-preserving and computationally-efficient federated misbehaviour detection technique that discriminates between natural power system disturbances and cyberattack events. Specifically, our designated solution leverages the use of a privacy-preserving gradient quantization-based scheme known as Differentially-Private Sign Stochastic Gradient Descent (DP-SIGNSGD) to improve the robustness of anomaly detection approaches against Byzantine attacks and improve computation efficiency. Empirical evaluations of our proposed framework on using publicly available industrial control systems datasets reveal superior attack detection rates whilst being resilient to Byzantine threats and computation-efficient as opposed to conventional FL strategies.