TY - GEN
T1 - Intrusion detection in smart grid measurement infrastructures based on principal component analysis
AU - Drayer, Elisabeth
AU - Routtenberg, Tirza
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - The extensive measurement infrastructure of smart grids is a vulnerable target for cyber attacks aiming at compromising reliable power supply. Thus, the detection of intrusion into the system and the identification of manipulated and false data is a key security capability required for future power systems. In this paper, we apply principal component analysis (PCA), together with a subspace analysis, to detect the presence of such false data injection (FDI) attacks. A key requirement for this method is a database of historical grid states that is used to compute the PCA transformation matrix. Each new grid state is then transformed based on this matrix to calculate its uncorrelated principal components. The presence of FDI attacks leads to a significant increase in the contribution of principal components that span the residual subspace. By comparing this projection against a threshold, the presence of compromised measurements can be detected. This is demonstrated by several case study simulations.
AB - The extensive measurement infrastructure of smart grids is a vulnerable target for cyber attacks aiming at compromising reliable power supply. Thus, the detection of intrusion into the system and the identification of manipulated and false data is a key security capability required for future power systems. In this paper, we apply principal component analysis (PCA), together with a subspace analysis, to detect the presence of such false data injection (FDI) attacks. A key requirement for this method is a database of historical grid states that is used to compute the PCA transformation matrix. Each new grid state is then transformed based on this matrix to calculate its uncorrelated principal components. The presence of FDI attacks leads to a significant increase in the contribution of principal components that span the residual subspace. By comparing this projection against a threshold, the presence of compromised measurements can be detected. This is demonstrated by several case study simulations.
UR - http://www.scopus.com/inward/record.url?scp=85068232621&partnerID=8YFLogxK
U2 - 10.1109/PTC.2019.8810858
DO - 10.1109/PTC.2019.8810858
M3 - Conference contribution
AN - SCOPUS:85068232621
T3 - 2019 IEEE Milan PowerTech, PowerTech 2019
BT - 2019 IEEE Milan PowerTech, PowerTech 2019
PB - Institute of Electrical and Electronics Engineers
T2 - 2019 IEEE Milan PowerTech, PowerTech 2019
Y2 - 23 June 2019 through 27 June 2019
ER -