TY - GEN
T1 - Load Redistribution Attack Detection using Machine Learning
T2 - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
AU - Pinceti, Andrea
AU - Sankar, Lalitha
AU - Kosut, Oliver
N1 - Funding Information:
VI. ACKNOWLEDGMENTS We would like to thank Mr. Zhigang Chu and Dr. Jiazi Zhang at ASU for their helpful guidance and support and for providing access to the attack design code. This material is based upon work supported by the National Science Foundation under Grant No. CNS-1449080.
Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. CNS-1449080
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/21
Y1 - 2018/12/21
N2 - Three detection techniques are presented against a wide class of cyber-attacks that maliciously redistribute loads by modifying measurements. The detectors use different anomaly detection algorithms based on machine learning techniques: nearest neighbor method, support vector machines, and replicator neural networks. The detectors are tested using a data-driven approach on a realistic dataset comprised of real historical load data in the form of publicly available PJM zonal data mapped to the IEEE 30-bus system. The results show all three detectors to be very accurate, with the nearest neighbor algorithm being the most computational efficient.
AB - Three detection techniques are presented against a wide class of cyber-attacks that maliciously redistribute loads by modifying measurements. The detectors use different anomaly detection algorithms based on machine learning techniques: nearest neighbor method, support vector machines, and replicator neural networks. The detectors are tested using a data-driven approach on a realistic dataset comprised of real historical load data in the form of publicly available PJM zonal data mapped to the IEEE 30-bus system. The results show all three detectors to be very accurate, with the nearest neighbor algorithm being the most computational efficient.
KW - Cybersecurity
KW - False data injection (FDI) attack
KW - Load redistribution attack
KW - Machine learning
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=85060784898&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060784898&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2018.8586644
DO - 10.1109/PESGM.2018.8586644
M3 - Conference contribution
AN - SCOPUS:85060784898
T3 - IEEE Power and Energy Society General Meeting
BT - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PB - IEEE Computer Society
Y2 - 5 August 2018 through 10 August 2018
ER -