Federated Learning-Based Explainable Anomaly Detection for Industrial Control Systems-Reference-Cited by-同舟云学术

Federated Learning-Based Explainable Anomaly Detection for Industrial Control Systems

Published:2022 Issue: Volume:10 Page:53854-53872
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Huong Truong Thu¹^ORCID,Bac Ta Phuong²,Ha Kieu Ngan¹^ORCID,Hoang Nguyen Viet¹,Hoang Nguyen Xuan¹,Hung Nguyen Tai¹,Tran Kim Phuc³^ORCID

Affiliation:

1. School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hai Ba Trung, Hanoi, Vietnam

2. School of Electronic Engineering, Soongsil University, Seoul, South Korea

3. Génie et Matériaux Textiles (GEMTEX), National Higher School of Arts and Textile Industries (ENSAIT), University of Lille, Lille, France

Funder

Hanoi University of Science and Technology

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/ielx7/6287639/9668973/09770834.pdf?arnumber=9770834

Reference30 articles.

1. Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach

2. Anomaly detection for water treatment system based on neural network with automatic architecture optimization;shalyga;arXiv 1807 07282,2018

3. Adversarial attacks on time-series intrusion detection for industrial control systems;zizzo;Proc IEEE 19th Int Conf Trust Secur Privacy Comput Commun (TrustCom),2021

4. MADICS: A Methodology for Anomaly Detection in Industrial Control Systems

5. Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

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