TY - GEN
T1 - Anomaly behavior analysis for smart grid automation system
AU - Orozco, Angel
AU - Pacheco, Jesus
AU - Hariri, Salim
N1 - Funding Information:
ACKNOWLEDGMENT This work is partly supported by the Air Force Office of Scientific Research (AFOSR) Dynamic Data-Driven Application Systems (DDDAS) award number FA95550-12-1-0241, National Science Foundation research projects NSF 1624668, SES-1314631, and DUE-1303362, and Thomson Reuters in the frame-work of the Partner University Fund (PUF) project (PUF is a program of the French Embassy in the United States and the FACE Foundation and is supported by American donors and the French government).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Urban Internet of Things systems are characterized by their application domain and they are designed to support the Smart City (SC) vision. The SC objective is to exploit advanced communication technologies to support the delivery of high quality services. A key element in a SC is the Smart Grid System (SGS), which is meant to be more efficient, reliable, and secure in managing electric power resources. SGS rely in the collection and analysis of data coming from devices such as sensors across the grid, which allow automated systems to perform advanced actions to accomplish its goals of efficiency and reliability. However, with the use of SGS, we are experiencing grand security challenges to protect such advanced and complex systems against errors and cyberattacks. In this work, we present an anomaly behavior analysis (ABA) system to detect and categorize several fault scenarios that may occur in SGSs. We tested our approach to detect normal operations, physical failures, and cyber-attacks. We applied our ABA methodology to a smart phasor measurement unit (PMU) to analyze, identify, and categorize the different SGS behaviors. The results show that our methodology can be used to accurately detect threats in both SGS and PMU with high detection rates and low false alarms.
AB - Urban Internet of Things systems are characterized by their application domain and they are designed to support the Smart City (SC) vision. The SC objective is to exploit advanced communication technologies to support the delivery of high quality services. A key element in a SC is the Smart Grid System (SGS), which is meant to be more efficient, reliable, and secure in managing electric power resources. SGS rely in the collection and analysis of data coming from devices such as sensors across the grid, which allow automated systems to perform advanced actions to accomplish its goals of efficiency and reliability. However, with the use of SGS, we are experiencing grand security challenges to protect such advanced and complex systems against errors and cyberattacks. In this work, we present an anomaly behavior analysis (ABA) system to detect and categorize several fault scenarios that may occur in SGSs. We tested our approach to detect normal operations, physical failures, and cyber-attacks. We applied our ABA methodology to a smart phasor measurement unit (PMU) to analyze, identify, and categorize the different SGS behaviors. The results show that our methodology can be used to accurately detect threats in both SGS and PMU with high detection rates and low false alarms.
KW - Internet of Things
KW - Phasor Measurement Unit
KW - Smart Cities
KW - anomaly behavior analysis
KW - smart grid
UR - http://www.scopus.com/inward/record.url?scp=85046280621&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046280621&partnerID=8YFLogxK
U2 - 10.1109/ROPEC.2017.8261614
DO - 10.1109/ROPEC.2017.8261614
M3 - Conference contribution
AN - SCOPUS:85046280621
T3 - 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
SP - 1
EP - 7
BT - 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Y2 - 8 November 2017 through 10 November 2017
ER -