Multigranularity Feature Automatic Marking-Based Deep Learning for Anomaly Detection of Industrial Control Systems-Reference-Cited by-同舟云学术

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Multigranularity Feature Automatic Marking-Based Deep Learning for Anomaly Detection of Industrial Control Systems

Published:2024 Issue: Volume:3 Page:1-10
ISSN:2768-7236
Container-title:IEEE Open Journal of Instrumentation and Measurement
language:
Short-container-title:IEEE Open J. Instrum. Meas.

Author:

Du Xinyi¹^ORCID,Xu Chi²^ORCID,Li Lin²,Li Xinchun¹^ORCID

Affiliation:

1. School of Electronic and Information Engineering, Liaoning Technical University, Huludao, China

2. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

Funder

National Natural Science Foundation of China

Science and Technology Program of Liaoning Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/9552935/10413235/10570378.pdf?arnumber=10570378

Reference24 articles.

1. Differential evolution-based convolutional neural networks: An automatic architecture design method for intrusion detection in industrial control systems

2. End-Edge Collaborative Lightweight Secure Federated Learning for Anomaly Detection of Wireless Industrial Control Systems

3. Machine Learning Methods for Industrial Protocol Security Analysis: Issues, Taxonomy, and Directions

4. An Enhanced Method for Reverse Engineering CAN Data Payload

5. WIA-NR: Ultra-Reliable Low-Latency Communication for Industrial Wireless Control Networks over Unlicensed Bands

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