An Efficient Federated Learning System for Network Intrusion Detection-Reference-Cited by-同舟云学术

An Efficient Federated Learning System for Network Intrusion Detection

Published:2023-06 Issue:2 Volume:17 Page:2455-2464
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Li Jianbin¹,Tong Xin¹,Liu Jinwei²^ORCID,Cheng Long¹^ORCID

Affiliation:

1. School of Control and Computer Engineering, North China Electric Power University, Beijing, China

2. Department of Computer and Information Sciences, Florida A&M University, Tallahassee, FL, USA

Funder

Ministry of Science and Technology Key R&D Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/10146391/10032055.pdf?arnumber=10032055

Reference31 articles.

1. Fast-Convergent Federated Learning With Adaptive Weighting

2. Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation

3. DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems

4. Multi-Task Network Anomaly Detection using Federated Learning

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