Abstract
With the rapid development of Internet of Things (IOT) technologies, modern power systems have become complex cyber-physical systems. A large number of smart devices have promoted efficient generation, transmission and distribution in the smart grid. State estimation (SE) is one of fundamental components in smart grid that evaluates the operation state of a grid by using a set of sensor measurements and grid topologies. A major issue is the authenticity of the measurements collected by the sensors. Specifically, the false data injection attack (FDIA) aims to temper the information that reflect the grid operation state. In this paper, we propose an efficient prediction-based FDIA detection and location scheme, PDL, in which the state vector of smart grid can be represented as multivariate time series, and can be predicted by vector autoregressive processes (VAR) through intentionally exploiting the temporal and spatial correlations of states. Different from most previous works which assumed the state transfer matrix constant and diagonal, a time-varying and non-diagonal matrix is adopted in this scheme. Then, the consistency between the predicted measurements and the observed measurements is utilized to detect and locate abnormal data. Besides, the detected abnormal data can be replaced with the predicted data, which simplifies the calibration process. Extensive simulation results verify the performance of the proposed scheme.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 |
| Editors | Claudio Demartini, Sorel Reisman, Ling Liu, Edmundo Tovar, Hiroki Takakura, Ji-Jiang Yang, Chung-Horng Lung, Sheikh Iqbal Ahamed, Kamrul Hasan, Thomas Conte, Motonori Nakamura, Zhiyong Zhang, Toyokazu Akiyama, William Claycomb, Stelvio Cimato |
| Publisher | IEEE Computer Society |
| Pages | 676-681 |
| Number of pages | 6 |
| Volume | 2 |
| ISBN (Electronic) | 9781538626665 |
| DOIs | |
| Publication status | Published - 2018 Jun 8 |
| Event | 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan Duration: 2018 Jul 23 → 2018 Jul 27 |
Other
| Other | 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 |
|---|---|
| Country | Japan |
| City | Tokyo |
| Period | 18/7/23 → 18/7/27 |
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Keywords
- False data injection attack
- Prediction
- Smart grid
- State estimation
ASJC Scopus subject areas
- Software
- Computer Science Applications
Cite this
PDL : An Efficient Prediction-Based False Data Injection Attack Detection and Location in Smart Grid. / Shi, Wanjiao; Wang, Yufeng; Jin, Qun; Ma, Jianhua.
Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. ed. / Claudio Demartini; Sorel Reisman; Ling Liu; Edmundo Tovar; Hiroki Takakura; Ji-Jiang Yang; Chung-Horng Lung; Sheikh Iqbal Ahamed; Kamrul Hasan; Thomas Conte; Motonori Nakamura; Zhiyong Zhang; Toyokazu Akiyama; William Claycomb; Stelvio Cimato. Vol. 2 IEEE Computer Society, 2018. p. 676-681 8377945.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - PDL
T2 - An Efficient Prediction-Based False Data Injection Attack Detection and Location in Smart Grid
AU - Shi, Wanjiao
AU - Wang, Yufeng
AU - Jin, Qun
AU - Ma, Jianhua
PY - 2018/6/8
Y1 - 2018/6/8
N2 - With the rapid development of Internet of Things (IOT) technologies, modern power systems have become complex cyber-physical systems. A large number of smart devices have promoted efficient generation, transmission and distribution in the smart grid. State estimation (SE) is one of fundamental components in smart grid that evaluates the operation state of a grid by using a set of sensor measurements and grid topologies. A major issue is the authenticity of the measurements collected by the sensors. Specifically, the false data injection attack (FDIA) aims to temper the information that reflect the grid operation state. In this paper, we propose an efficient prediction-based FDIA detection and location scheme, PDL, in which the state vector of smart grid can be represented as multivariate time series, and can be predicted by vector autoregressive processes (VAR) through intentionally exploiting the temporal and spatial correlations of states. Different from most previous works which assumed the state transfer matrix constant and diagonal, a time-varying and non-diagonal matrix is adopted in this scheme. Then, the consistency between the predicted measurements and the observed measurements is utilized to detect and locate abnormal data. Besides, the detected abnormal data can be replaced with the predicted data, which simplifies the calibration process. Extensive simulation results verify the performance of the proposed scheme.
AB - With the rapid development of Internet of Things (IOT) technologies, modern power systems have become complex cyber-physical systems. A large number of smart devices have promoted efficient generation, transmission and distribution in the smart grid. State estimation (SE) is one of fundamental components in smart grid that evaluates the operation state of a grid by using a set of sensor measurements and grid topologies. A major issue is the authenticity of the measurements collected by the sensors. Specifically, the false data injection attack (FDIA) aims to temper the information that reflect the grid operation state. In this paper, we propose an efficient prediction-based FDIA detection and location scheme, PDL, in which the state vector of smart grid can be represented as multivariate time series, and can be predicted by vector autoregressive processes (VAR) through intentionally exploiting the temporal and spatial correlations of states. Different from most previous works which assumed the state transfer matrix constant and diagonal, a time-varying and non-diagonal matrix is adopted in this scheme. Then, the consistency between the predicted measurements and the observed measurements is utilized to detect and locate abnormal data. Besides, the detected abnormal data can be replaced with the predicted data, which simplifies the calibration process. Extensive simulation results verify the performance of the proposed scheme.
KW - False data injection attack
KW - Prediction
KW - Smart grid
KW - State estimation
UR - http://www.scopus.com/inward/record.url?scp=85055548295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055548295&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC.2018.10317
DO - 10.1109/COMPSAC.2018.10317
M3 - Conference contribution
AN - SCOPUS:85055548295
VL - 2
SP - 676
EP - 681
BT - Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
A2 - Demartini, Claudio
A2 - Reisman, Sorel
A2 - Liu, Ling
A2 - Tovar, Edmundo
A2 - Takakura, Hiroki
A2 - Yang, Ji-Jiang
A2 - Lung, Chung-Horng
A2 - Ahamed, Sheikh Iqbal
A2 - Hasan, Kamrul
A2 - Conte, Thomas
A2 - Nakamura, Motonori
A2 - Zhang, Zhiyong
A2 - Akiyama, Toyokazu
A2 - Claycomb, William
A2 - Cimato, Stelvio
PB - IEEE Computer Society
ER -