Machine Learning Methods for Attack Detection in the Smart Grid

Download

index.pdf

Date

2016-08-01

Author

Ozay, Mete
Esnaola, Inaki
Yarman Vural, Fatoş Tunay
Kulkarni, Sanjeev R.
Poor, H. Vincent

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

122
views

67
downloads

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.

Subject Keywords

Sparse optimization, Smart grid security, Phase transition, Classification, Attack detection

URI

https://hdl.handle.net/11511/41230

Journal

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

DOI

https://doi.org/10.1109/tnnls.2015.2404803

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Distributed Models for Sparse Attack Construction and State Vector Estimation in the Smart Grid Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatoş Tunay; Kulkarni, Sanjeev R.; Poor, H. Vincent (2012-11-08) Two distributed attack models and two distributed state vector estimation methods are introduced to handle the sparsity of smart grid networks in order to employ unobservable false data injection attacks and estimate state vectors. First, Distributed Sparse Attacks in which attackers process local measurements in order to achieve consensus for an attack vector are introduced. In the second attack model, called Collective Sparse Attacks, it is assumed that the topological information of the network and the m...
Smarter Security in the Smart Grid Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatoş Tunay; Kulkarni, Sanjeev R.; Poor, H. Vincent (2012-11-08) A new formulation for detection of false data injection attacks in the smart grid is introduced. The attack detection problem is posed as a statistical learning problem in which the observed measurements are classified as being either attacked or secure. The proposed approach provides an attack detection framework that surmounts over the constraints arising due to the sparse structure of the problem and implicitly exploits any available prior knowledge about the system. Specifically, three supervised learni...
Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2012-05-17) In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using a passive sensor network. Non-cooperative transmissions from illuminators of opportunity like GSM base stations, FM radio transmitters or digital broadcasters are exploited by non-directional separately located Doppler measuring sensors. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target s...
Multi-target tracking using passive doppler measurements Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26) In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.
Covariance Matrix Estimation of Texture Correlated Compound-Gaussian Vectors for Adaptive Radar Detection Candan, Çağatay; Pascal, Frederic (2022-01-01) Covariance matrix estimation of compound-Gaussian vectors with texture-correlation (spatial correlation for the adaptive radar detectors) is examined. The texture parameters are treated as hidden random parameters whose statistical description is given by a Markov chain. States of the chain represent the value of texture coefficient and the transition probabilities establish the correlation in the texture sequence. An Expectation-Maximization (EM) method based covariance matrix estimation solution is given ...

Citation Formats

M. Ozay, I. Esnaola, F. T. Yarman Vural, S. R. Kulkarni, and H. V. Poor, “Machine Learning Methods for Attack Detection in the Smart Grid,” IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, pp. 1773–1786, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41230.