Cross-Layered Distributed Data-Driven Framework for Enhanced Smart Grid Cyber-Physical Security

Starke, Allen; Nagaraj, Keerthiraj; Ruben, Cody; Aljohani, Nader; Zou, Sheng; Bretas, Arturo Suman; Mcnair, Janise; Zare, Alina

doi:10.1049/stg2.12070

Cross-Layered Distributed Data-Driven Framework for Enhanced Smart Grid Cyber-Physical Security

Journal Article · Thu May 26 04:00:00 UTC 2022 · IET Smart Grid

DOI:https://doi.org/10.1049/stg2.12070· OSTI ID:1871333

Starke, Allen ^[1]; Nagaraj, Keerthiraj ^[1]; Ruben, Cody ^[2]; Aljohani, Nader ^[1]; Zou, Sheng ^[1]; ^[3]; Mcnair, Janise ^[1]; Zare, Alina ^[1]

University of Florida
Burns & McDonnell
BATTELLE (PACIFIC NW LAB)

Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber-threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real-time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross-layer approach. In this paper a cross-layered strategy is presented, called Cross-Layer Ensemble CorrDet with Adaptive Statistics(CECD-AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score compared to current approaches that only use SG measurement data for detection such as the traditional physics-based State Estimation, Ensemble CorrDet with Adaptive Statistics strategy and other machine learning classification-based detection schemes.

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-76RL01830

OSTI ID:: 1871333

Report Number(s):: PNNL-SA-171621

Journal Information:: IET Smart Grid, Journal Name: IET Smart Grid

Country of Publication:: United States

Language:: English

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