Network Threat Awareness Model with Smart Grid for Big Data Analysis

The smart grid benefits greatly from advanced data processing and communications technology. But security threats continue to interfere with information systems and they also affect smart grids. The smart grid's existing security efforts concentrate on conventional detection and prevention techniques. On the other hand, many attacks happen rapidly and are not detected by outdated security measures. These threats generally interrupt the regular operation of the smart grid and have significant effects on it. Furthermore, it may be difficult to reverse the damage and it is too late to take measures against threats once they are identified. To address this problem, the research implements a security situation awareness system that is based on big data analysis within the smart grid. The security situational analysis for the smart grid is performed by efficiently integrating reinforcement learning, game theory, and fuzzy cluster based analytical approach. When compared to existing method of True Data Integrity by Agent-Based Model (TDI-ABM), implemented mechanism achieved high normal and attack behavior identification, and attained low - false rate of normal and attack behavior.