Federated Learning Inspired Low-Complexity Intrusion Detection and Classification Technique for SDN-Based Industrial CPS
Published 2023 View Full Article
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Title
Federated Learning Inspired Low-Complexity Intrusion Detection and Classification Technique for SDN-Based Industrial CPS
Authors
Keywords
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Journal
IEEE Transactions on Network and Service Management
Volume 20, Issue 3, Pages 2442-2459
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Online
2023-07-29
DOI
10.1109/tnsm.2023.3299606
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Related references
Note: Only part of the references are listed.- On the Reliability of Industrial Internet of Things from Systematic Perspectives: Evaluation Approaches, Challenges, and Open Issues
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- Intelligent software defined networking: Long short term memory‐graded rated unit enabled block‐attack model to tackle distributed denial of service attacks
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- RFDOA-Net: An Efficient ConvNet for RF-Based DOA Estimation in UAV Surveillance Systems
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- Intelligent Intrusion Detection Based on Federated Learning for Edge-Assisted Internet of Things
- (2021) Dapeng Man et al. Security and Communication Networks
- A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things
- (2021) Ruijie Zhao et al. IEEE Internet of Things Journal
- Intrusion Detection Based on Privacy-Preserving Federated Learning for the Industrial IoT
- (2021) Pedro Ruzafa-Alcazar et al. IEEE Transactions on Industrial Informatics
- Network Intrusion Detection and Comparative Analysis Using Ensemble Machine Learning and Feature Selection
- (2021) Saikat Das et al. IEEE Transactions on Network and Service Management
- DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems
- (2020) Beibei Li et al. IEEE Transactions on Industrial Informatics
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