Visible to the public Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter

TitleDetection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter
Publication TypeJournal Article
Year of Publication2014
AuthorsManandhar, K., Xiaojun Cao, Fei Hu, Yao Liu
JournalControl of Network Systems, IEEE Transactions on
Volume1
Pagination370-379
Date PublishedDec
ISSN2325-5870
Keywordscommunication infrastructure, computer network security, Cyber physical system, denial-of-service attack, dependent variables, Detectors, DoS attack, Euclidean detector, false data injection attack, false data-injection attack, fault diagnosis, Kalman filter, Kalman filter smart grid, Kalman filters, Mathematical model, power engineering computing, power system security, predictor variables, random attack, robust security framework, security, Smart grids, smart power grids, smart-grid systems security, state processes, χ2-detector
Abstract

By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of smart-grid systems, with techniques such as denial-of-service (DoS) attack, random attack, and data-injection attack. In this paper, we present a mathematical model of the system to study these pitfalls and propose a robust security framework for the smart grid. Our framework adopts the Kalman filter to estimate the variables of a wide range of state processes in the model. The estimates from the Kalman filter and the system readings are then fed into the h2-detector or the proposed Euclidean detector. The h2-detector is a proven effective exploratory method used with the Kalman filter for the measurement of the relationship between dependent variables and a series of predictor variables. The h2-detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the h2-detector is unable to detect the statistically derived false data-injection attack. To overcome this limitation, we prove that the Euclidean detector can effectively detect such a sophisticated injection attack.

URLhttp://ieeexplore.ieee.org/document/6897944/
DOI10.1109/TCNS.2014.2357531
Citation Key6897944