Machine Learning Methods for Attack Detection in the Smart Grid

• Published in: IEEE transaction on neural networks and learning systems Vol. 27; no. 8; pp. 1773 - 1786
• Main Authors: Ozay, Mete, Esnaola, Inaki, Yarman Vural, Fatos Tunay, Kulkarni, Sanjeev R., Poor, H. Vincent
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Attack detection; classification; Distance education; Distance learning; Kernel; Learning; Learning systems; Machine learning; Machine learning algorithms; Mathematical analysis; Neural networks; phase transition; Prediction algorithms; Smart grid; smart grid security; Smart grids; sparse optimization; State vectors; Statistical learning; Vectors
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2015.2404803

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.