Work

Joint-Transformation-Based Detection of False Data Injection Attacks in Smart Grid

Year: 2017

Type: article

Abstract: For reliable operation and control of smart grid, estimating the correct states is of utmost importance to the system operator. With recent incorporation of information technology and advanced metering infrastructure, the futuristic grid is more prone to cyber-threats. The false data injection (FDI) attack is one of the most thoroughly researched cyber-attacks. Intelligently crafted, it can cause false estimation of states, which further seriously affects the entire power system operation. In this paper, we propose joint-transformation-based scheme to detect FDI attacks in real time. The proposed method is built on the dynamics of measurement variations. Kullback–Leibler distance is used to find out the difference between probability distributions obtained from measurement variations. The proposed method is tested using IEEE 14 bus system considering attack on different state variables. The results shows that the proposed scheme detects FDI attacks with high detection probability.

Source: IEEE Transactions on Industrial Informatics

DOI: 10.1109/tii.2017.2720726

Authors: Sandeep Kumar Singh, Kush Khanna, Ranjan Bose, Bijaya Ketan Panigrahi, Anupam Joshi

Institutions: Indian Institute of Technology Delhi, University of Maryland, Baltimore County

Language: English

Topic: Smart Grid Security and Resilience

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: green

OpenAlex ID: W2735539676

FWCI: 9.793

Cites: 30

Cited by: 181

Related to: 10