A federated learning method for network intrusion detection-Reference-Cited by-同舟云学术

A federated learning method for network intrusion detection

Published:2021-12-24 Issue: Volume: Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Tang Zhongyun¹²,Hu Haiyang¹,Xu Chonghuan³⁴⁵^ORCID

Affiliation:

1. School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

2. School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China

3. School of Business Administration Zhejiang Gongshang University Hangzhou China

4. Academy of Zhejiang Culture Industry Innovation and Development Zhejiang Gongshang University Hangzhou China

5. Modern Business Research Center Zhejiang Gongshang University Hangzhou China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6812

Reference56 articles.

1. LuntTF JagannathanAR.A prototype real‐time intrusion‐detection expert system. Paper presented at: IEEE Symposium on Security and Privacy; Vol. 59; Oakland CA; April 18‐21 1988.

2. Research on intrusion detection and response: a survey;Kabiri P;Int J Netw Secur,2005

3. A survey of deep learning‐based network anomaly detection;Kwon D;Cluster Comput,2017

4. A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System

5. A Multicloud-Model-Based Many-Objective Intelligent Algorithm for Efficient Task Scheduling in Internet of Things

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