Detecting Attacks in the Smart Grid Using Mac...

Detecting Attacks in the Smart Grid Using Machine Learning Algorithm

Author Name : Dr. P. Pedda Sadhu Naik

ABSTRACT: In the assault detection, smart grid challenges are provided as statistical tasks to learn for various scenario of an attack, with measurements taken in a batch or in an online environment. Methods of machine learning are utilised in this approach to identify measurements as secure or attacked. In the suggested technique, a framework for detecting attacks is offered to use any previous experience information about the system and overcome constraints deriving due to the problem's sparse structure. To simulate the attack detection problem, popular batch and real-time (semi-supervised and supervised) learning methods are integrated with fusion at the decision as well as feature levels. The statistical and geometrical correlations characteristics attack vectors used in attack scenarios, as well as learning algorithms investigated in order to use statistical learning methods to detect unobservable attacks. On a variety of IEEE test systems, the proposed approaches are put to the test. The findings of the experiments show that in the proposed attack detection framework, machine learning techniques outperform detection algorithms for attacks that use strategies for estimating state vectors.