A hierarchical framework using approximated local outlier factor for efficient anomaly detection

Lin Xu, Yi Ren Yeh, Yuh-Jye Lee, Jing Li

Research output: Contribution to journalConference articlepeer-review

25 Scopus citations


Anomaly detection aims to identify rare events that deviate remarkably from existing data. To satisfy real-world applications, various anomaly detection technologies have been proposed. Due to the resource constraints, such as limited energy, computation ability and memory storage, most of them cannot be directly used in wireless sensor networks (WSNs). In this work, we proposed a hierarchical anomaly detection framework to overcome the challenges of anomaly detection in WSNs. We aim to detect anomalies by the accurate model and the approximated model learned at the remote server and sink nodes, respectively. Besides the framework, we also proposed an approximated local outlier factor algorithm, which can be learned at the sink nodes. The proposed algorithm is more efficient in computation and storage by comparing with the standard one. Experimental results verify the feasibility of our proposed method in terms of both accuracy and efficiency.

Original languageEnglish
Pages (from-to)1174-1181
Number of pages8
JournalProcedia Computer Science
StatePublished - 2013
Event4th International Conference on Ambient Systems, Networks and Technologies, ANT 2013 and the 3rd International Conference on Sustainable Energy Information Technology, SEIT 2013 - Halifax, NS, Canada
Duration: 25 Jun 201328 Jun 2013


  • Anomaly detection
  • Hamming distance
  • Local outlier factor
  • Local sensitive hashing


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