Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection

Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection

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In a physical microgrid system, equipment failures, manual misbehavior of equipment, and power quality can be affected by intentional cyberattacks, made more dangerous by the widespread use of established communication networks via sensors.This paper comprehensively reviews smart grid challenges on cyber-physical and cyber security systems, standard protocols, communication, and sensor technology.Existing supervised learning-based Machine Learning (ML) methods for identifying cyberattacks in smart grids mostly rely on instances of both normal and attack events for training.Additionally, for supervised learning to be effective, the training dataset must contain representative examples of various attack situations 2014 dodge ram 1500 fender flares having different patterns, which is challenging.

Therefore, we reviewed a novel Data Mining (DM) approach based on unsupervised rules for identifying False Data Injection Cyber wella color charm 050 cooling violet Attacks (FDIA) in smart grids using Phasor Measurement Unit (PMU) data.The unsupervised algorithm is excellent for discovering unidentified assault events since it only uses examples of typical events to train the detection models.The datasets used in our study, which looked at some well-known unsupervised detection methods, helped us assess the performances of different methods.The performance comparison with popular unsupervised algorithms is better at finding attack events if compared with supervised and Deep Learning (DL) algorithms.