Malicious Traffic Detection With Noise Labels Based on Cross-Modal Consistency-Reference-Cited by-同舟云学术

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Malicious Traffic Detection With Noise Labels Based on Cross-Modal Consistency

Published:2024-06 Issue:2 Volume:6 Page:148-151
ISSN:2576-3156
Container-title:IEEE Networking Letters
language:
Short-container-title:IEEE Netw. Lett.

Author:

Yuan Qingjun¹^ORCID,Niu Weina²^ORCID,Wang Yongjuan¹^ORCID,Gou Gaopeng³,Lu Bin¹^ORCID

Affiliation:

1. Henan Key Laboratory of Network Cryptography Technology, Zhengzhou, China

2. School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu, China

3. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

Funder

National Key Research and Development Program of China

Blockchain System Security Key Technology Research of Henan Province Major Public Welfare Project

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8253410/10551648/10379482.pdf?arnumber=10379482

Reference13 articles.

1. Enhanced Echo-State Restricted Boltzmann Machines for Network Traffic Prediction

2. A survey of network-based intrusion detection data sets

3. Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

4. Machine Learning for Encrypted Malware Traffic Classification

5. Co-teaching: Robust training of deep neural networks with extremely noisy labels;Han

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