Traffic Classification Method Based on Federated Semi-Supervised Learning

Author:

Sun Chongxin1ORCID,Chen Bo1ORCID,Bu Youjun1ORCID,Zhang Desheng1ORCID

Affiliation:

1. Information Technology Institute, PLA Strategic Support Force Information Engineering University, China

Publisher

ACM

Reference29 articles.

1. Hong-Ning Dai , Raymond Chi-Wing Wong , Hao Wang, Zibin Zheng, and Athanasios V. Vasilakos. 2019 . Big Data Analytics for Large-scale Wireless Networks: Challenges and Opportunities. ACM Comput. Surv. 52, 5, Article 99 (September 2020), 36 pages. https://doi.org/10.1145/3337065 10.1145/3337065 Hong-Ning Dai, Raymond Chi-Wing Wong, Hao Wang, Zibin Zheng, and Athanasios V. Vasilakos. 2019. Big Data Analytics for Large-scale Wireless Networks: Challenges and Opportunities. ACM Comput. Surv. 52, 5, Article 99 (September 2020), 36 pages. https://doi.org/10.1145/3337065

2. Ang Rong , Ma Chunguang , and Wu Peng . An Intrusion Detection Method Based on Federated Learning and Convolutional Neural Network . 2020 , Netinfo Security , 20 ( 4 ): 47 - 54 . Ang Rong, Ma Chunguang, and Wu Peng. An Intrusion Detection Method Based on Federated Learning and Convolutional Neural Network. 2020, Netinfo Security, 20(4): 47-54.

3. Zhao Ying , Wang LiBao , CHEN JunJun , Network anomaly detection based on federated learning . 2021 . Journal of Beijing University of Chemical Technology (Natural Science) , 48(2): 92 − 99. Zhao Ying, Wang LiBao, CHEN JunJun, Network anomaly detection based on federated learning. 2021. Journal of Beijing University of Chemical Technology (Natural Science), 48(2): 92 − 99.

4. Mohammad Mehedi Hassan , Abdu Gumaei , Ahmed Alsanad , Majed Alrubaian , and Giancarlo Fortino . 2020. A hybrid deep learning model for efficient intrusion detection in big data environment. Inform. Sciences ( 2020 ), 386-396. DOI 10.1016/j.ins.2019.10.069 Mohammad Mehedi Hassan, Abdu Gumaei, Ahmed Alsanad, Majed Alrubaian, and Giancarlo Fortino. 2020. A hybrid deep learning model for efficient intrusion detection in big data environment. Inform. Sciences (2020), 386-396. DOI 10.1016/j.ins.2019.10.069

5. Wonyong Jeong , Jaehong Yoon , Eunho Yang , and Sung Ju Hwang Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning . Cornell University Library, arXiv.org , Ithaca , 2021 . Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning. Cornell University Library, arXiv.org, Ithaca, 2021.

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. Incremental federated learning for traffic flow classification in heterogeneous data scenarios;Neural Computing and Applications;2024-08-12

2. Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classification;2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC);2023-09-18

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