Anomaly Detection for Discrete Sequences: A Survey
Oak Ridge National Laboratory · University of Minnesota
Abstract
This survey attempts to provide a comprehensive and structured overview of the existing research for the problem of detecting anomalies in discrete/symbolic sequences. The objective is to provide a global understanding of the sequence anomaly detection problem and how existing techniques relate to each other. The key contribution of this survey is the classification of the existing research into three distinct categories, based on the problem formulation that they are trying to solve. These problem formulations are: 1) identifying anomalous sequences with respect to a database of normal sequences; 2) identifying an anomalous subsequence within a long sequence; and 3) identifying a pattern in a sequence whose…
Citation impact
- FWCI
- 44.26
- Percentile
- 100%
- References
- 123
Authors
3Topics & keywords
- Anomaly detection
- Computer science
- Subsequence
- Relevance (law)
- Sequence (biology)
- Categorization
- Data mining
- Key (lock)