A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems-Reference-Cited by-同舟云学术

A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems

Published:2023 Issue: Volume:11 Page:86977-86998
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Elnour Mariam¹^ORCID,Noorizadeh Mohammad¹^ORCID,Shakerpour Mohammad²^ORCID,Meskin Nader¹^ORCID,Khan Khaled²^ORCID,Jain Raj³^ORCID

Affiliation:

1. Department of Electrical Engineering, Qatar University, Doha, Qatar

2. Department of Computer Science and Engineering, Qatar University, Doha, Qatar

3. Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA

Funder

National Priorities Research Programme (NPRP) through the Qatar National Research Fund

Qatar National Library

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10210375.pdf?arnumber=10210375

Reference81 articles.

1. DAISY: A Detection and Mitigation System Against Denial-of-Service Attacks in Software-Defined Networks

2. Resilient operation of DC microgrid against FDI attack: A GRU based framework

3. A Dual-Isolation-Forests-Based Attack Detection Framework for Industrial Control Systems

4. Extended observer‐based hybrid tracking control strategy for networked system with FDI attacks

5. A DDoS Attack Detection and Mitigation With Software-Defined Internet of Things Framework

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