Privacy-Preserving FDIA Detection for Radial Distribution Systems Considering Attacker Preference

False Data Injection Attack (FDIA) is one of the major cybersecurity patterns in distribution system state estimation. To detect FDIAs while protecting the data privacy, this paper proposes a distributed data-driven FDIA detection method for the three-phase unbalanced distribution system. Considering the data privacy and professional attackers' preference, the stealthiness and concealment of a polynomial pseudo-measurement-based FDIA is deduced and demonstrated. An alternating direction method of multipliers is improved based on the auto-tuned penalty coefficient (namely ρ-ADMM). The effectiveness of the proposed distributed detection algorithm is verified by setting different attack intensities in the IEEE 123 distribution system. Numerical comparisons are provided including validations of FDIA detection accuracy, data recovery, voltage unbalance analysis, and time-consumption.