Securing Cyber-Physical Industrial Systems Against False Data Injection Attacks: A Hybrid Detection Approach

Khalil Guibene; Nadhir Messai; Marwane Ayaida

doi:10.1109/bcca58897.2023.10338925

ScienceGate Book Chapters

JOURNAL ARTICLE

Securing Cyber-Physical Industrial Systems Against False Data Injection Attacks: A Hybrid Detection Approach

Khalil Guibene Nadhir Messai Marwane Ayaida

Year: 2023 Pages: 142-149

DOI: 10.1109/bcca58897.2023.10338925

Get Full-Text PDF Get Analytical Report

Abstract

International audience

Keywords:

Cyber-physical system Testbed Computer science Industrial control system Anomaly detection Fuse (electrical) Decision tree Artificial neural network Data mining Real-time computing Computer security Embedded system Artificial intelligence Computer network Control (management) Engineering

Metrics

Cited By

0.75

FWCI (Field Weighted Citation Impact)

Refs

0.69

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Smart Grid Security and Resilience

Physical Sciences → Engineering → Control and Systems Engineering

Electrostatic Discharge in Electronics

Physical Sciences → Engineering → Electrical and Electronic Engineering

Physical Unclonable Functions (PUFs) and Hardware Security

Physical Sciences → Computer Science → Hardware and Architecture

Securing Cyber-Physical Industrial Systems Against False Data Injection Attacks: A Hybrid Detection Approach

Abstract

Metrics

Citation History

Topics

Related Documents

Securing Industrial Control Systems Against Cyber-Physical Attacks

Stochastic Stealthy False Data Injection Attacks Against Cyber-Physical Systems

Datadriven false data injection attacks against cyber-physical power systems

Collaborative Detection Method against False Data Injection Attacks in Microgrid Cyber-Physical Systems

Detection of False Data Injection Attacks in Cyber-Physical Systems