Toward Ever-Evolution Network Threats: A Hierarchical Federated Class-Incremental Learning Approach for Network Intrusion Detection in IIoT-Reference-Cited by-同舟云学术

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Toward Ever-Evolution Network Threats: A Hierarchical Federated Class-Incremental Learning Approach for Network Intrusion Detection in IIoT

Published:2024-09-15 Issue:18 Volume:11 Page:29864-29877
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Mao Jingxin¹^ORCID,Wei Zhiwei²^ORCID,Li Bing¹^ORCID,Zhang Rongqing¹^ORCID,Song Lingyang³^ORCID

Affiliation:

1. School of Software Engineering, Tongji University, Shanghai, China

2. Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China

3. National Engineering Laboratory for Big Data Analysis and Applications, School of Electronics, Peking University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission

Hangzhou Qianjiang Special Expert and the Science and Technology Innovation Program of Hunan Province

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6488907/10660568/10546981.pdf?arnumber=10546981

Reference36 articles.

1. Research on the Whole Process Security System Framework of Network Collaborative Manufacture

2. A collaborative architecture of the industrial internet platform for manufacturing systems

3. An In-Depth Analysis of IoT Security Requirements, Challenges, and Their Countermeasures via Software-Defined Security

4. Network Intrusion Detection for IoT Security Based on Learning Techniques

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