Poison-Resilient Anomaly Detection: Mitigating Poisoning Attacks in Semi-Supervised Encrypted Traffic Anomaly Detection-Reference-Cited by-同舟云学术

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Poison-Resilient Anomaly Detection: Mitigating Poisoning Attacks in Semi-Supervised Encrypted Traffic Anomaly Detection

Published:2024-09 Issue:5 Volume:11 Page:4744-4757
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Wu Zhangfa¹^ORCID,Li Huifang¹^ORCID,Qian Yekui²^ORCID,Hua Yi³^ORCID,Gan Hongping⁴^ORCID

Affiliation:

1. School of Electronic Information, Northwestern Polytechnical University, Xi'an, China

2. People's Liberation Army Air Force Academy, Zhengzhou, China

3. School of Aeronautics, Northwestern Polytechnical University, Xi'an, China

4. School of Software, Northwestern Polytechnical University, Xi'an, China

Funder

Innovation Workstation of State Key Laboratory of Intelligent Gaming

Basic Research Programs of Taicang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488902/10637788/10528791.pdf?arnumber=10528791

Reference45 articles.

1. The Security Impact of HTTPS Interception

2. Implementation of deep packet inspection in smart grids and industrial Internet of Things: Challenges and opportunities

3. BlindBox

4. TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder

5. Automatic Detection of DGA-Enabled Malware Using SDN and Traffic Behavioral Modeling

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