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

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 (AMI), the futuristic grid is more prone to cyber-threats. The False Data Injection (FDI) attack is one of the most thoroughly researched cyberattacks. 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 (KLD) 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.

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

“…From a complete (or even partial) familiarity with the power network topology, a smart attacker can add biased data to meter or sensor-collected measurements Z by forming an attack vector, a = [a 1 , a 2 , ..., a m ] T , to deceive the bad data detector [27]. Let Z a = Z + a be the measurements containing the attacked data.…”

“…To train the DAE model, commonly used choices for the addition of corruption are the zero-masking DAE (ZDAE) and the additive Gaussian DAE (GDAE) [27]. In addition to these schemes, we have introduced another corruption-addition scheme termed estimated DAE (EDAE).…”

Covert Cyber Assault Detection in Smart Grid Networks Utilizing Feature Selection and Euclidean Distance-Based Machine Learning

“…Therefore in the context of SG cyber-security and computational complexity, the selection of distinctive features becomes a promising strategy to detect the CCD assault in real time [5]. Sandeep et al [6] proposed a joint-transformation-based scheme to detect CCD assault. They utilized the Kullback-Leibler distance to find out the difference between probability distributions obtained from measurement variations.…”

“…In summary, existing works on CCD assault detection in SGs only have generally considered feature extraction or transformation [5][6][7] in the context of cybersecurity and the curse of dimensionality. To the best of our knowledge, the selection of distinguishing features from the SE-MF dataset in the context of SG security is still an open problem.…”

“…M. Ozay et al [5] utilized a variety of machine learning-based schemes to detect the CCD assault in SG. Sandeep et al [6] proposed joint-transformation-based scheme to detect CCD assault. They utilize the Kullback-Leibler distance to find out the difference between probability distributions obtained from measurement variations.…”