A data-based detection method against false data injection attacks

Charalambos Konstantinou; Michail Maniatakos

doi:10.1109/MDAT.2019.2952357

A data-based detection method against false data injection attacks

Charalambos Konstantinou, Michail Maniatakos

Research output: Contribution to journal › Article › peer-review

Abstract

Editor's notes: CPSs are vulnerable to process-aware attacks that aim to disrupt the proper functioning or hamper performance/efficiency/stability/safety of the physical systems/processes of the CPSs. This article considers utilization of state estimators in smart grids for detection of false data injection attacks using data-driven anomaly detection. Based on a local outlier factor approach, it is shown that false data injection attacks can be reliably detected without requiring prior information on power system parameters or topology. Simulation studies on an IEEE 14-bus system show the efficacy of the approach. - Farshad Khorrami, New York University.

Original language	English (US)
Article number	8894484
Pages (from-to)	67-74
Number of pages	8
Journal	IEEE Design and Test
Volume	37
Issue number	5
DOIs	https://doi.org/10.1109/MDAT.2019.2952357
State	Published - Oct 2020

Keywords

Cybersecurity
dimensionality reduction
false data injection attacks
outlier detection
state estimation

ASJC Scopus subject areas

Software
Hardware and Architecture
Electrical and Electronic Engineering

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10.1109/MDAT.2019.2952357

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A data-based detection method against false data injection attacks

Abstract

Keywords

ASJC Scopus subject areas

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