Selection of Deep Neural Network Models for IoT Anomaly Detection Experiments
Author:
Funder
Russian Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9406783/9407036/09407189.pdf?arnumber=9407189
Cited by 12 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. Multi-task learning for IoT traffic classification: A comparative analysis of deep autoencoders;Future Generation Computer Systems;2024-09
2. Machine Learning Methods and Other Methods for Detecting Network Threats to IoT Devices;2024 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF);2024-06-03
3. Hybrid System for Monitoring the Traffic Consumption of IoT Devices;2024 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF);2024-06-03
4. Hybrid Multi-Task Deep Learning for Improved IoT Network Intrusion Detection: Exploring Different CNN Structures;2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS);2024-01-03
5. Anomaly detection using deep convolutional generative adversarial networks in the internet of things;ISA Transactions;2023-12
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