A detection method for false data injection attacks in power systems based on artificial fish swarm K-means clustering algorithm

With the improving the intelligence level of traditional power systems, power cyber physical system also faces increasingly serious network security issues. For the detection of false data injection attacks (FDIA), A detection method based on artificial fish swarm-K-means (AFS-K-means) clustering is introduced. This method utilizes artificial fish swarm algorithm to generate initial cluster centers for the K-means clustering, thereby resolving the heavy reliance of the traditional K-means clustering algorithm on initial cluster centers and improving the accuracy of FDIA detection. A simulation is conducted based on the IEEE-30 bus system, and the result indicates that the proposed AFS-K-means clustering algorithm can effectively detect FDIAs, and exhibits superior performance comparing to other similar methods.