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

arXiv: https://arxiv.org/abs/2111.05460 · HTML: https://mlsanity.com/paper/2111.05460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 51 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Cyber-Physical Systems Security -- A Survey](https://mlsanity.com/paper/1701.04525/) (2017)
- [Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)](https://mlsanity.com/paper/1603.06140/) (2016)
- [Hybrid data-driven physics model-based framework for enhance cyber-physical smart grid security](https://mlsanity.com/paper/1910.10197/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)

## Abstract (arXiv)

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.