Data Requirements for Anomaly Detection

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Authors
  1. Horn, S.
  2. Eisler, C.
  3. Dobias, P.
  4. Collins, J.
Corporate Authors
Defence Research and Development Canada, Centre for Operational Research and Analysis, Ottawa ON (CAN)
Abstract
New processing techniques are being developed to extract and highlight anomalous maritime behaviour by leveraging the abundance of open source and/or commercially available information on a global scale. This common approach to data science relies on the exploitation of large datasets through methods such as data analytics and data mining. In the reverse aspect, one can instead explicitly consider the definition and types of anomalies that a maritime security operator would desire to know about to derive the quantity of data required to achieve a given level of confidence in detection. The requirements gap between the available information and the desired effect can be identified by working the problem of anomaly detection from both ends: exploiting the data available, and quantifying the desired end state. This work presents a framework for the definition of data requirements for a set of operationally relevant anomalies. By formally quantifying the data gaps, resource investment for additional data can be better directed in order to improve operational utility of the dataset.
Keywords
large data;anomaly detection;space-based AIS
Report Number
DRDC-RDDC-2016-P082 — External Literature
Date of publication
16 Sep 2016
Number of Pages
5
DSTKIM No
CA043126
CANDIS No
804414
Format(s):
Electronic Document(PDF)

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