Deep Isolation Forest for Anomaly Detection-Reference-Cited by-同舟云学术

Deep Isolation Forest for Anomaly Detection

Published:2023-12-01 Issue:12 Volume:35 Page:12591-12604
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Xu Hongzuo¹^ORCID,Pang Guansong²^ORCID,Wang Yijie¹^ORCID,Wang Yongjun³^ORCID

Affiliation:

1. Science and Technology on Parallel and Distributed Processing Laboratory, College of Computer, National University of Defense Technology, Changsha, Hunan, China

2. School of Computing and Information Systems, Singapore Management University, Singapore

3. College of Computer, National University of Defense Technology, Changsha, Hunan, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Science Foundation of Ministry of Education of China

Postgraduate Scientific Research Innovation Project of Hunan Province

Science Fund for Distinguished Young Scholars of Hunan Province

Singapore Ministry of Education Academic Research Fund Tier 1

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10311056/10108034.pdf?arnumber=10108034

Reference50 articles.

1. Unsupervised representation learning by predicting random distances;wang;Proc Int Joint Conf Artif Intell,2021

2. Classification-based anomaly detection for general data;bergman;Proc Int Conf Learn Representations,2020

3. Deep Anomaly Detection with Deviation Networks

4. RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection

5. Deep one-class classification;ruff;Proc Int Conf Mach Learn,2018

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