Fast Anomaly Detection for Streaming Data

Tan, Swee Chuan; Ting, Kai Ming; Liu, Fei Tony

doi:10.5591/978-1-57735-516-8/IJCAI11-254

Fast Anomaly Detection for Streaming Data

Swee Chuan Tan, Kai Ming Ting, Fei Tony Liu

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1511-1515.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-254

Abstract

This paper introduces Streaming Half-Space-Trees (HS-Trees), a fast one-class anomaly detector for evolving data streams. It requires only normal data for training and works well when anomalous data are rare. The model features an ensemble of random HS-Trees, and the tree structure is constructed without any data. This makes the method highly efficient because it requires no model restructuring when adapting to evolving data streams. Our analysis shows that Streaming HS-Trees has constant amortised time complexity and constant memory requirement. When compared with a state-of-the-art method, our method performs favourably in terms of detection accuracy and runtime performance. Our experimental results also show that the detection performance of Streaming HS-Trees is not sensitive to its parameter settings.

Swee Chuan Tan, Kai Ming Ting, and Fei Tony Liu. 2011. Fast Anomaly Detection for Streaming Data. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1511–1515. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-254

@inproceedings{ijcai2011p254,
  title     = {Fast Anomaly Detection for Streaming Data},
  author    = {Tan, Swee Chuan and Ting, Kai Ming and Liu, Fei Tony},
  booktitle = {Proceedings of the Twenty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-11}},
  publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
  editor    = {Toby Walsh},
  pages     = {1511--1515},
  year      = {2011},
  month     = {7},
  doi       = {10.5591/978-1-57735-516-8/IJCAI11-254},
  url       = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-254},
}

TY  - CPAPER
TI  - Fast Anomaly Detection for Streaming Data
AU  - Tan, Swee Chuan
AU  - Ting, Kai Ming
AU  - Liu, Fei Tony
T2  - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11
PB  - AAAI Press/International Joint Conferences on Artificial Intelligence
PY  - 2011
SP  - 1511
EP  - 1515
DO  - 10.5591/978-1-57735-516-8/IJCAI11-254
UR  - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-254
ER  -