Data Integrity Attack Detection Using Ensemble-Based Learning for Cyber–Physical Power Systems

The modern power system has evolved into a cyber-physical system with the integration of PMUs, smart meters, substation automation, and wide-area monitoring and control systems. This increases attack surface area and cybersecurity issues, but we can also use these technologies to prevent such scenarios. This paper provides a novel technique to generate and detect data integrity attacks in smart grids. It also gives an optimization algorithm for generating FDIA against state estimation algorithms present at the control center. The formulation for generating AC state estimation attack with full information and limited information along with DC state estimation attacks is given. It further proposes a combining technique for the voting based ensemble learning technique (MVCC) to detect FDIA in smartgrids. The model is then tested on an IEEE 24 bus system and 39 bus new England system by generating false data injection attacks and detecting them. The detection strategy is compared with most of the existing state of the art Machine learning algorithms, ensemble algorithms and conventional weighted least square algorithm and is found to have a better performance.