Reinforcement Learning Meets Network Intrusion Detection: A Transferable and Adaptable Framework for Anomaly Behavior Identification-Reference-Cited by-同舟云学术

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Reinforcement Learning Meets Network Intrusion Detection: A Transferable and Adaptable Framework for Anomaly Behavior Identification

Published:2024-04 Issue:2 Volume:21 Page:2477-2492
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

He Mingshu¹^ORCID,Wang Xiaojuan²^ORCID,Wei Peng²,Yang Liu²^ORCID,Teng Yinglei²,Lyu Renjian³

Affiliation:

1. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China

2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China

3. Information Science Academy, China Electronics Technology Group Corporation, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4275028/10499985/10399344.pdf?arnumber=10399344

Reference70 articles.

1. Investigation of Machine Learning Techniques in Intrusion Detection System for IoT Network

2. PERT: Payload Encoding Representation from Transformer for Encrypted Traffic Classification

3. Real-Time Network Intrusion Detection System Based on Deep Learning

4. Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection

5. Intrusion Detection System Using PCA with Random Forest Approach

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. Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset;Future Internet;2024-08-08

2. Comparative Analysis of Deep Convolutional Neural Network—Bidirectional Long Short-Term Memory and Machine Learning Methods in Intrusion Detection Systems;Applied Sciences;2024-08-08

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