Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security

TL;DR

This paper introduces a cross-layered framework called CECD-AS that enhances smart grid cyber-physical security by detecting various cyber-attacks through integrated analysis of measurement data and network anomalies, achieving high detection accuracy.

Contribution

The paper presents a novel cross-layered detection strategy that combines measurement data and network metrics for improved anomaly detection in smart grids, outperforming existing methods.

Findings

CECD-AS detects multiple attack types with high F1-score.

It outperforms traditional physics-based and machine learning detection schemes.

The approach enhances reliability and accuracy of smart grid security.

Abstract

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.