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Title:Detecting Anomalies by Data Aggregation in the Power Grid
Author(s):Nguyen, Hoang; Nahrstedt, Klara
Subject(s):PowerGrid
data aggregation
computer science
Abstract:The August 2003 Blackout event showed that the PowerGrid is vulnerable to the cyber attacks. The event also showed the need for automatic detection of abnormal behaviors in the PowerGrid. In this paper, we propose a solution for the problem of detecting anomaly behaviors such as reporting incorrect status of the devices of the PowerGrid. First, we apply the non-parametric Cumulative Sum algorithm for quickest detection of the value-changing problem. Then, to improve the speed of detection, we explore the use of two data aggregation schemes: average-aggregation and quantize-aggregation. Our analytical and simulation results show that end-to-end detection delay should include not only the semantic computation of the data but also the underlying communication networks. Finally, we show that it is possible to improve the end-to-end detection delay by data aggregation.
Issue Date:2006-07
Genre:Technical Report
Type:Text
URI:http://hdl.handle.net/2142/11237
Other Identifier(s):UIUCDCS-R-2006-2755
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-21


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