AUTH: An Adversarial Autoencoder Based Unsupervised Insider Threat Detection Scheme for Multisource Logs-Reference-Cited by-同舟云学术

AUTH: An Adversarial Autoencoder Based Unsupervised Insider Threat Detection Scheme for Multisource Logs

Published:2024-09 Issue:9 Volume:20 Page:10954-10965
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Zhu Xingjian¹^ORCID,Dong Jiankuo²^ORCID,Qi Jin³^ORCID,Zhou Zhenguo¹^ORCID,Dong Zhenjiang²^ORCID,Sun Yanfei⁴^ORCID,Wang Moyu⁵^ORCID

Affiliation:

1. College of Automation & College Of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China

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

3. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China

4. Department of Internet of Things and Jiangsu Engineering Research Center of HPC and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, China

5. Hefei Yun Micro Electronics Company, Ltd, Hefei, China

Funder

National Key Research and Development Program of China

Primary Research & Development Plan of Jiangsu Province

National Natural Science Foundation of China

General Program of the National Science Foundation of China

Natural Science Research Project of Colleges and Universities in Jiangsu Province

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10666802/10535047.pdf?arnumber=10535047

Reference48 articles.

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2. Cybersecurity awareness in the context of the Industrial Internet of Things: A systematic literature review

3. Botnet attacks classification in AMI networks with recursive feature elimination (RFE) and machine learning algorithms

4. BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning

5. Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques