Distributed Device-Specific Anomaly Detection for Resource-Constrained Devices
Author:
Affiliation:
1. Technical University of Munich
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10154252/10154208/10154372.pdf?arnumber=10154372
Reference15 articles.
1. Distributed device-specific anomaly detection using deep feed-forward neural networks;lübben;NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium,2023
2. Real-Time IoT Device Activity Detection in Edge Networks
3. The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set
4. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
5. Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Similarity-Based Selective Federated Learning for Distributed Device-Specific Anomaly Detection;NOMS 2024-2024 IEEE Network Operations and Management Symposium;2024-05-06
2. Distributed Device-Specific Anomaly Detection for Resource-Constrained Devices;NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium;2023-05-08
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