OPHiForest: Order Preserving Hashing Based Isolation Forest for Robust and Scalable Anomaly Detection-Reference-Cited by-同舟云学术

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OPHiForest: Order Preserving Hashing Based Isolation Forest for Robust and Scalable Anomaly Detection

Published:2020-10-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Information & Knowledge Management
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Author:

Xiang Haolong¹,Salcic Zoran¹,Dou Wanchun²,Xu Xiaolong³,Qi Lianyong⁴,Zhang Xuyun⁵

Affiliation:

1. The University of Auckland, Auckland, New Zealand

2. Nanjing University, Nanjing, China

3. Nanjing University of Information Science and Technology, Nanjing, China

4. Qufu Normal University, Qufu, China

5. Macquarie University, Sydney, NSW, Australia

Funder

the UoA FRDF fund

the New Zealand Marsden Fund

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3340531.3411988

Reference32 articles.

1. Theoretical Foundations and Algorithms for Outlier Ensembles

2. Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble

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1. AD2S: Adaptive anomaly detection on sporadic data streams;Computer Communications;2023-09

2. DeepiForest: A Deep Anomaly Detection Framework with Hashing Based Isolation Forest;2022 IEEE International Conference on Data Mining (ICDM);2022-11

3. Self-supervision Meets Adversarial Perturbation;Proceedings of the 31st ACM International Conference on Information & Knowledge Management;2022-10-17

4. Detecting anomalies within smart buildings using do-it-yourself internet of things;Journal of Ambient Intelligence and Humanized Computing;2022-09-24

5. Enhanced Performance of Finitie Boundary Isolation Forest (FBIF) for Datasets with Standard Distribution Properties;2022 International Conference on Electrical, Computer and Energy Technologies (ICECET);2022-07-20

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