TY - GEN
T1 - AMIDS
T2 - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012
AU - McLaughlin, Stephen
AU - Holbert, Brett
AU - Zonouz, Saman
AU - Berthier, Robin
PY - 2012
Y1 - 2012
N2 - The advanced metering infrastructure (AMI) is a crucial component of the smart grid, replacing traditional analog devices with computerized smart meters. Smart meters have not only allowed for efficient management of many end-users, but also have made AMI an attractive target for remote exploits and local physical tampering with the end goal of stealing energy. While smart meters posses multiple sensors and data sources that can indicate energy theft, in practice, the individual methods exhibit many false positives. In this paper, we present AMIDS, an AMI intrusion detection system that uses information fusion to combine the sensors and consumption data from a smart meter to more accurately detect energy theft. AMIDS combines meter audit logs of physical and cyber events with consumption data to more accurately model and detect theft-related behavior. Our experimental results on normal and anomalous load profiles show that AMIDS can identify energy theft efforts with high accuracy. Furthermore, AMIDS correctly identified legitimate load profile changes that more elementary analyses classified as malicious.
AB - The advanced metering infrastructure (AMI) is a crucial component of the smart grid, replacing traditional analog devices with computerized smart meters. Smart meters have not only allowed for efficient management of many end-users, but also have made AMI an attractive target for remote exploits and local physical tampering with the end goal of stealing energy. While smart meters posses multiple sensors and data sources that can indicate energy theft, in practice, the individual methods exhibit many false positives. In this paper, we present AMIDS, an AMI intrusion detection system that uses information fusion to combine the sensors and consumption data from a smart meter to more accurately detect energy theft. AMIDS combines meter audit logs of physical and cyber events with consumption data to more accurately model and detect theft-related behavior. Our experimental results on normal and anomalous load profiles show that AMIDS can identify energy theft efforts with high accuracy. Furthermore, AMIDS correctly identified legitimate load profile changes that more elementary analyses classified as malicious.
UR - http://www.scopus.com/inward/record.url?scp=84876029987&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876029987&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/SmartGridComm.2012.6486009
DO - https://doi.org/10.1109/SmartGridComm.2012.6486009
M3 - Conference contribution
SN - 9781467309110
T3 - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012
SP - 354
EP - 359
BT - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012
Y2 - 5 November 2012 through 8 November 2012
ER -