FLNET2023: Realistic Network Intrusion Detection Dataset for Federated Learning
Author:
Affiliation:
1. New Mexico State University,Las Cruces,New Mexico,USA
2. DEVCOM Analysis Center,WSMR,New Mexico,USA
Funder
U.S. Department of Energy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10356123/10356124/10356272.pdf?arnumber=10356272
Reference15 articles.
1. Communication-efficient learning of deep networks from decentralized data;McMahan,2017
2. Federated mimic learning for privacy preserving intrusion detection;Al-Marri,2020
3. Internet of Things Intrusion Detection: Centralized, On-Device, or Federated Learning?
4. FLDDoS: DDoS Attack Detection Model based on Federated Learning
5. A detailed analysis of the KDD CUP 99 data set
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2. Enhancing IoT Intrusion Detection with Federated Learning-Based CNN-GRU and LSTM-GRU Ensembles;2024 19th International Symposium on Wireless Communication Systems (ISWCS);2024-07-14
3. Enhancing Intrusion Detection Systems Using a Deep Learning and Data Augmentation Approach;Systems;2024-03-01
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