FLDDoS: DDoS Attack Detection Model based on Federated Learning
Author:
Affiliation:
1. Institute of Systems and Networks, Nankai University, College of Computer Science,Tianjin,China
2. Shijiazhuang Huayan Traffic Technology Co., Ltd.
Funder
Natural Science Foundation of Tianjin
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9724288/9724267/09724339.pdf?arnumber=9724339
Reference19 articles.
1. Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection
2. Deep Learning-Based DDoS-Attack Detection for Cyber–Physical System Over 5G Network
3. Multi-Armed Bandit-Based Client Scheduling for Federated Learning
4. Optimizing Federated Learning on Non-IID Data with Reinforcement Learning
5. Unsupervised Federated Learning for Unbalanced Data
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1. 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
2. Privacy-Preserving Detection of DDoS Attacks in IoT Using Federated Learning Techniques;2024 IEEE International Conference on Big Data & Machine Learning (ICBDML);2024-02-24
3. IoT Malicious Traffic Detection Based on Federated Learning;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024
4. Securing 5G/6G IoT Using Transformer and Personalized Federated Learning: An Access-Side Distributed Malicious Traffic Detection Framework;IEEE Open Journal of the Communications Society;2024
5. FLNET2023: Realistic Network Intrusion Detection Dataset for Federated Learning;MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM);2023-10-30
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