Finding Component Relationships: A Deep-Learning-Based Anomaly Detection Interpreter-Reference-Cited by-同舟云学术

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Finding Component Relationships: A Deep-Learning-Based Anomaly Detection Interpreter

Published:2024-06 Issue:3 Volume:11 Page:4149-4162
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Xu Lijuan¹^ORCID,Han Ziyu¹^ORCID,Wang Zhen²^ORCID,Zhao Dawei¹^ORCID

Affiliation:

1. Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

2. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Shaanxi, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6570650/10557215/10443463.pdf?arnumber=10443463

Reference45 articles.

1. Virus Propagation and Patch Distribution in Multiplex Networks: Modeling, Analysis, and Optimal Allocation

2. PLC-SEIFF: A programmable logic controller security incident forensics framework based on automatic construction of security constraints

3. Analysis of Recent Deep-Learning-Based Intrusion Detection Methods for In-Vehicle Network

4. Anomaly transformer: Time series anomaly detection with association discrepancy;Xu,2022

5. MST-GAT: A multimodal spatial–temporal graph attention network for time series anomaly detection

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