Smarter Security in the Smart Grid

Ozay, Mete
Esnaola, Inaki
Yarman Vural, Fatoş Tunay
Kulkarni, Sanjeev R.
Poor, H. Vincent
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 learning algorithms are presented. These procedures operate by first observing the power system in order to construct a training dataset which is later used to detect the attacks in new observations. In order to assess the validity of the proposed techniques, the behavior of the proposed algorithms is examined on IEEE test systems.
IEEE 3rd International Conference on Smart Grid Communications (SmartGridComm)


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...
Machine Learning Methods for Attack Detection in the Smart Grid
Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatoş Tunay; Kulkarni, Sanjeev R.; Poor, H. Vincent (2016-08-01)
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...
Effective training set sampling strategy for SVDD anomaly detection in hyperspectral imagery
Ergul, Mustafa; Sen, Nigar; Okman, O. Erman (2014-05-07)
Anomaly detection (AD) is an important application for target detection in remotely sensed hyperspectral data. Therefore, variety kinds of methods with different advantages and drawbacks have been proposed for past two decades. Recently, the kernelized support vector data description (SVDD) based anomaly detection approaches has become popular as these methods avoid prior assumptions about the distribution of data and provides better generalization to characterize the background. The global SVDD needs a tra...
Partial encryption of video for communication and storage
Yüksel, Turan; Yarman Vural, Fatoş Tunay; Akar, Gözde Bozdağı; Department of Computer Engineering (2003)
In this study, a new method is proposed to protect video data through partial encryption. Unlike previous methods, the bit rate of the encrypted portion can be controlled. In order to accomplish this task, a simple model for the time to break the partial encryption by a chipertext-only attack is defined. Then, the encrypted bit budget distribution strategy maximizing the time subject to the bitrate constraint is found. An algorithm to estimate the model parameters is constructed and it isthen implemented ov...
Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models
Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatoş Tunay; Kulkarni, Sanjeev R.; Poor, H. Vincent (Institute of Electrical and Electronics Engineers (IEEE), 2013-07-01)
New methods that exploit sparse structures arising in smart grid networks are proposed for the state estimation problem when data injection attacks are present. First, construction strategies for unobservable sparse data injection attacks on power grids are proposed for an attacker with access to all network information and nodes. Specifically, novel formulations for the optimization problem that provide a flexible design of the trade-off between performance and false alarm are proposed. In addition, the ce...
Citation Formats
M. Ozay, I. Esnaola, F. T. Yarman Vural, S. R. Kulkarni, and H. V. Poor, “Smarter Security in the Smart Grid,” presented at the IEEE 3rd International Conference on Smart Grid Communications (SmartGridComm), Tainan, TAIWAN, 2012, Accessed: 00, 2020. [Online]. Available: