High-Stealth False Data Attacks on Overloading Multiple Lines in Power Systems
Min Du, Lianhong Wang, Yicong Zhou
Abstract
In this letter, we present a single mixed-integer linear programming (MILP) model for high-stealth false data attacks (FDAs) on overloading a set of lines by injecting stealthy false data. The proposed model reveals that an intelligent attacker is able to deliberately construct a valid attack vector to overload multiple transmission lines while hiding it among normal data to evade advanced anomaly detection methods. In addition, the proposed cyber-attack mode can help the attacker optimally select the targeted lines. Simulation results on multiple large-scale test systems validate the effectiveness of the proposed approach.
Topics & Concepts
Computer scienceElectric power transmissionInteger programmingConstruct (python library)Set (abstract data type)Anomaly detectionData modelingData setElectric power systemData miningPower (physics)AlgorithmEngineeringArtificial intelligenceComputer networkPhysicsQuantum mechanicsElectrical engineeringDatabaseProgramming languageSmart Grid Security and ResiliencePower Systems Fault DetectionElectricity Theft Detection Techniques