CMTSNN: A Deep Learning Model for Multiclassification of Abnormal and Encrypted Traffic of Internet of Things-Reference-Cited by-同舟云学术

CMTSNN: A Deep Learning Model for Multiclassification of Abnormal and Encrypted Traffic of Internet of Things

Published:2023-07-01 Issue:13 Volume:10 Page:11773-11791
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhu Shizhou¹,Xu Xiaolong²^ORCID,Gao Honghao³^ORCID,Xiao Fu¹^ORCID

Affiliation:

1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

2. Jiangsu Key Laboratory of Big Data Security and Intelligent Processing and the School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

3. School of Computer Engineering and Science, Shanghai University, Shanghai, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10159464/10044190.pdf?arnumber=10044190

Reference37 articles.

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2. An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks

3. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset

4. Efficient Keyword Matching for Deep Packet Inspection based Network Traffic Classification

5. Deep learning methods in network intrusion detection: A survey and an objective comparison;gamage;J Netw Comput Appl,2020

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