FS-IDS: A Novel Few-Shot Learning Based Intrusion Detection System for SCADA Networks-Reference-Cited by-同舟云学术

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FS-IDS: A Novel Few-Shot Learning Based Intrusion Detection System for SCADA Networks

Published:2021-06 Issue: Volume: Page:
ISSN:
Container-title:ICC 2021 - IEEE International Conference on Communications
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Author:

Ouyang Yuankai,Li Beibei,Kong Qinglei,Song Han,Li Tao

Funder

Research and Development

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9500243/9500244/09500667.pdf?arnumber=9500667

Cited by 13 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. Toward a Reliable Network-Based Intrusion Detection Model for SCADA: A Classification with Reject Option Approach;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Classifying attack traffic in IoT environments via few-shot learning;Journal of Information Security and Applications;2024-06

3. NFHP-RN: A Method of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet;Computer Modeling in Engineering & Sciences;2024

4. Survey and Analysis of Intrusion Detection Frameworks for Cyber-Physical Systems: A Comprehensive Study;Lecture Notes in Electrical Engineering;2024

5. Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods;IEEE Transactions on Machine Learning in Communications and Networking;2024

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