OPHiForest

Order preserving hashing based isolation forest for robust and scalable anomaly detection

Haolong Xiang, Zoran Salcic, Wanchun Dou, Xiaolong Xu, Lianyong Qi, Xuyun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Anomaly detection is one of the most important data mining tasks in many real-life applications such as network intrusion detection for cybersecurity and medical diagnosis for healthcare. In the big data era, these applications demand fast and versatile anomaly detection capability to handle various types of increasingly huge-volume data. However, existing detection methods are either slow due to high computational complexity, or unable to deal with complicated anomalies like local anomalies. In this paper, we propose a novel anomaly detection method named OPHiForest with the use of the order preserving hashing based isolation forest. The core idea is to learn the information from data to construct better isolation forest structure than the state-of-the-art methods like iForest and LSHiForest, which can achieve robust detection of various anomaly types. We design a fast two-step learning process for the order preserving hashing scheme. This leads to stronger order preservation for better hashing, and therefore enhances anomaly detection robustness and accuracy. Extensive experiments on both synthetic and real-world data sets demonstrate that our method is highly robust and scalable.

Original languageEnglish
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages1655-1664
Number of pages10
ISBN (Print)9781450368599
DOIs
Publication statusPublished - 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: 19 Oct 202023 Oct 2020

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
CountryIreland
CityVirtual, Online
Period19/10/2023/10/20

Keywords

  • anomaly detection
  • isolation forest
  • OPHiForest
  • order preservation
  • robust and scalable

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