TY - JOUR
T1 - A data-based detection method against false data injection attacks
AU - Konstantinou, Charalambos
AU - Maniatakos, Michail
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-13
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Editor's notes: CPSs are vulnerable to process-aware attacks that aim to disrupt the proper functioning or hamper performance/efficiency/stability/safety of the physical systems/processes of the CPSs. This article considers utilization of state estimators in smart grids for detection of false data injection attacks using data-driven anomaly detection. Based on a local outlier factor approach, it is shown that false data injection attacks can be reliably detected without requiring prior information on power system parameters or topology. Simulation studies on an IEEE 14-bus system show the efficacy of the approach. - Farshad Khorrami, New York University.
AB - Editor's notes: CPSs are vulnerable to process-aware attacks that aim to disrupt the proper functioning or hamper performance/efficiency/stability/safety of the physical systems/processes of the CPSs. This article considers utilization of state estimators in smart grids for detection of false data injection attacks using data-driven anomaly detection. Based on a local outlier factor approach, it is shown that false data injection attacks can be reliably detected without requiring prior information on power system parameters or topology. Simulation studies on an IEEE 14-bus system show the efficacy of the approach. - Farshad Khorrami, New York University.
UR - https://ieeexplore.ieee.org/document/8894484/
UR - http://www.scopus.com/inward/record.url?scp=85074852785&partnerID=8YFLogxK
U2 - 10.1109/MDAT.2019.2952357
DO - 10.1109/MDAT.2019.2952357
M3 - Article
SN - 2168-2364
VL - 37
SP - 67
EP - 74
JO - IEEE Design and Test
JF - IEEE Design and Test
IS - 5
ER -
