Early anomaly detection in an interconnected power grid and communication network: exploiting interdependent structure of failures

Dong Hoon Shin, Junshan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

We study data fusion schemes for early detection of anomalies in an interconnected power grid and communication network, where power nodes rely on the real-time control via communication nodes, which in turn, depend on the former for power supply. Based on a key observation that failures are spatially correlated and propagate through neighboring nodes, we propose a data fusion scheme, which »scans» an anomalous cluster, i.e., a connected component of nodes, in each individual network. We show that the proposed scheme can detect weaker signals of anomalies, compared to baseline approaches, and further its detection capability increases with the size of the anomalous cluster. This finding leads us to further exploit the interdependent structure of failures across two networks and design a more powerful data fusion scheme, which jointly detects an anomalous cluster over the two interconnected networks. To analyze the detection gain of the joint data fusion scheme, we first characterize how quickly the anomalous behavior propagates, in both the power grid and the power- communication network, based on random graph and epidemic models. We then present numerical results to quantify the detection gain of the joint data fusion scheme.

Original languageEnglish (US)
Title of host publication2015 IEEE Global Communications Conference, GLOBECOM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479959525
DOIs
StatePublished - Feb 23 2016
Event58th IEEE Global Communications Conference, GLOBECOM 2015 - San Diego, United States
Duration: Dec 6 2015Dec 10 2015

Other

Other58th IEEE Global Communications Conference, GLOBECOM 2015
CountryUnited States
CitySan Diego
Period12/6/1512/10/15

Fingerprint

Data fusion
Telecommunication networks
communication
Real time control
supply
Communication

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication

Cite this

Shin, D. H., & Zhang, J. (2016). Early anomaly detection in an interconnected power grid and communication network: exploiting interdependent structure of failures. In 2015 IEEE Global Communications Conference, GLOBECOM 2015 [7417493] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOCOM.2014.7417493

Early anomaly detection in an interconnected power grid and communication network : exploiting interdependent structure of failures. / Shin, Dong Hoon; Zhang, Junshan.

2015 IEEE Global Communications Conference, GLOBECOM 2015. Institute of Electrical and Electronics Engineers Inc., 2016. 7417493.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shin, DH & Zhang, J 2016, Early anomaly detection in an interconnected power grid and communication network: exploiting interdependent structure of failures. in 2015 IEEE Global Communications Conference, GLOBECOM 2015., 7417493, Institute of Electrical and Electronics Engineers Inc., 58th IEEE Global Communications Conference, GLOBECOM 2015, San Diego, United States, 12/6/15. https://doi.org/10.1109/GLOCOM.2014.7417493
Shin DH, Zhang J. Early anomaly detection in an interconnected power grid and communication network: exploiting interdependent structure of failures. In 2015 IEEE Global Communications Conference, GLOBECOM 2015. Institute of Electrical and Electronics Engineers Inc. 2016. 7417493 https://doi.org/10.1109/GLOCOM.2014.7417493
Shin, Dong Hoon ; Zhang, Junshan. / Early anomaly detection in an interconnected power grid and communication network : exploiting interdependent structure of failures. 2015 IEEE Global Communications Conference, GLOBECOM 2015. Institute of Electrical and Electronics Engineers Inc., 2016.
@inproceedings{d6f09bf3b96746c1a44a29eda486637f,
title = "Early anomaly detection in an interconnected power grid and communication network: exploiting interdependent structure of failures",
abstract = "We study data fusion schemes for early detection of anomalies in an interconnected power grid and communication network, where power nodes rely on the real-time control via communication nodes, which in turn, depend on the former for power supply. Based on a key observation that failures are spatially correlated and propagate through neighboring nodes, we propose a data fusion scheme, which »scans» an anomalous cluster, i.e., a connected component of nodes, in each individual network. We show that the proposed scheme can detect weaker signals of anomalies, compared to baseline approaches, and further its detection capability increases with the size of the anomalous cluster. This finding leads us to further exploit the interdependent structure of failures across two networks and design a more powerful data fusion scheme, which jointly detects an anomalous cluster over the two interconnected networks. To analyze the detection gain of the joint data fusion scheme, we first characterize how quickly the anomalous behavior propagates, in both the power grid and the power- communication network, based on random graph and epidemic models. We then present numerical results to quantify the detection gain of the joint data fusion scheme.",
author = "Shin, {Dong Hoon} and Junshan Zhang",
year = "2016",
month = "2",
day = "23",
doi = "10.1109/GLOCOM.2014.7417493",
language = "English (US)",
booktitle = "2015 IEEE Global Communications Conference, GLOBECOM 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Early anomaly detection in an interconnected power grid and communication network

T2 - exploiting interdependent structure of failures

AU - Shin, Dong Hoon

AU - Zhang, Junshan

PY - 2016/2/23

Y1 - 2016/2/23

N2 - We study data fusion schemes for early detection of anomalies in an interconnected power grid and communication network, where power nodes rely on the real-time control via communication nodes, which in turn, depend on the former for power supply. Based on a key observation that failures are spatially correlated and propagate through neighboring nodes, we propose a data fusion scheme, which »scans» an anomalous cluster, i.e., a connected component of nodes, in each individual network. We show that the proposed scheme can detect weaker signals of anomalies, compared to baseline approaches, and further its detection capability increases with the size of the anomalous cluster. This finding leads us to further exploit the interdependent structure of failures across two networks and design a more powerful data fusion scheme, which jointly detects an anomalous cluster over the two interconnected networks. To analyze the detection gain of the joint data fusion scheme, we first characterize how quickly the anomalous behavior propagates, in both the power grid and the power- communication network, based on random graph and epidemic models. We then present numerical results to quantify the detection gain of the joint data fusion scheme.

AB - We study data fusion schemes for early detection of anomalies in an interconnected power grid and communication network, where power nodes rely on the real-time control via communication nodes, which in turn, depend on the former for power supply. Based on a key observation that failures are spatially correlated and propagate through neighboring nodes, we propose a data fusion scheme, which »scans» an anomalous cluster, i.e., a connected component of nodes, in each individual network. We show that the proposed scheme can detect weaker signals of anomalies, compared to baseline approaches, and further its detection capability increases with the size of the anomalous cluster. This finding leads us to further exploit the interdependent structure of failures across two networks and design a more powerful data fusion scheme, which jointly detects an anomalous cluster over the two interconnected networks. To analyze the detection gain of the joint data fusion scheme, we first characterize how quickly the anomalous behavior propagates, in both the power grid and the power- communication network, based on random graph and epidemic models. We then present numerical results to quantify the detection gain of the joint data fusion scheme.

UR - http://www.scopus.com/inward/record.url?scp=84964897485&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964897485&partnerID=8YFLogxK

U2 - 10.1109/GLOCOM.2014.7417493

DO - 10.1109/GLOCOM.2014.7417493

M3 - Conference contribution

AN - SCOPUS:84964897485

BT - 2015 IEEE Global Communications Conference, GLOBECOM 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -