University of Illinois Urbana-Champaign

Detecting Anomalies by Data Aggregation in the Power Grid

Nguyen, Hoang; Nahrstedt, Klara

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Detecting Anomalies by Data Aggregation in the Power Grid.pdf

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https://hdl.handle.net/2142/11237

Description

Title
Detecting Anomalies by Data Aggregation in the Power Grid
Author(s)
  • Nguyen, Hoang
  • Nahrstedt, Klara
Issue Date
2006-07
Keyword(s)
  • PowerGrid
  • data aggregation
  • computer science
Date of Ingest
2009-04-21T16:16:53Z
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.
Type of Resource
text
Genre of Resource
Technical Report
Permalink
http://hdl.handle.net/2142/11237
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