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Searching for Unknown Anomalies in Hierarchical Data Streams
- Source :
- IEEE Signal Processing Letters. 28:1774-1778
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- We consider the problem of anomaly detection among a large number of processes, where the probabilistic models of anomalies are unknown. At each time, aggregated noisy observations can be taken from a chosen subset of processes, where the chosen subset conforms to a tree structure. The observation distribution depends on the chosen subset and the absence/presence of anomalies. We develop a sequential search strategy using a hierarchical Kolmogorov-Smirnov (KS) statistics. Referred to as Tree-based Anomaly Search using KS statistics (TASKS), the proposed strategy is order-optimal with respect to the size of the search space and the detection accuracy.
- Subjects :
- Computer science
business.industry
Applied Mathematics
Anomaly (natural sciences)
Probabilistic logic
Pattern recognition
Hierarchical database model
Tree structure
Distribution (mathematics)
Signal Processing
Signal processing algorithms
Anomaly detection
Artificial intelligence
Electrical and Electronic Engineering
business
Linear search
Subjects
Details
- ISSN :
- 15582361 and 10709908
- Volume :
- 28
- Database :
- OpenAIRE
- Journal :
- IEEE Signal Processing Letters
- Accession number :
- edsair.doi...........c05c1d599f47db69c31b41dc97a65af2