Analysis on Detecting Cyber Security Attacks Using Deep Ensemble Learning on Smart Grids

Abstract

In recent times, cyber security offers a significant advancement in smart grid technologies for its availability and functionality. The potential intrusion in smart grids marks the system to behave in a vulnerable way all the private data. Smart grids are often prone to data integrity attacks at its physical layer, which is been a critical issue presently. This attack alters the measurement of compromised meter set by the attacker(s). It misleads the decision making by the operators at the control center and thereby the reliability of the measurement is affected. In this chapter, the authors present a deep learning ensemble (DLE) model that possibly detects the potential data integrity attacks in the physical layer. The deep learning model uses ensemble learning to make decisions and combines the classified results to improve the classification on test data. The experiments are conducted on the proposed DLE model to find the accuracy of classifier the malicious and benign measurements.