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 language | English (US) |
|---|---|
| Title of host publication | 2015 IEEE Global Communications Conference, GLOBECOM 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781479959525 |
| DOIs | |
| State | Published - Feb 23 2016 |
| Event | 58th IEEE Global Communications Conference, GLOBECOM 2015 - San Diego, United States Duration: Dec 6 2015 → Dec 10 2015 |
Other
| Other | 58th IEEE Global Communications Conference, GLOBECOM 2015 |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 12/6/15 → 12/10/15 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering
- Communication
Cite this
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 proceeding › Conference contribution
}
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 -