Anomaly detection using the Kullback-Leibler divergence metric

Mostafa Afgani, Sinan Sinanovic, Harald Haas

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    A method of detecting changes or anomalies in periodic information-carrying signals or any other sets of data using Kullback-Leibler divergence is described. Theoretical reasons for using this information-theoretic approach are briefly outlined and followed by its detailed application on disturbance/anomaly detection in wireless signals. Even though the concept is illustrated in a communications centric framework, it is more generally applicable in areas such as computational neuroscience, mathematical finance and others where it is important to statistically detect unexpected signal distortions. The results obtained show that the proposed approach is robust, highly effective, and has a low implementation complexity.
    Original languageEnglish
    Title of host publication1st International Symposium on Applied Sciences on Biomedical and Communication Technologies (ISABEL '08)
    PublisherIEEE
    Pages1-5
    Number of pages5
    ISBN (Print)9781424426478
    DOIs
    Publication statusPublished - 2008

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    Keywords

    • wireless signals
    • computational neuroscience
    • Kullback-Leibler divergence

    Cite this

    Afgani, M., Sinanovic, S., & Haas, H. (2008). Anomaly detection using the Kullback-Leibler divergence metric. In 1st International Symposium on Applied Sciences on Biomedical and Communication Technologies (ISABEL '08) (pp. 1-5). IEEE. https://doi.org/10.1109/ISABEL.2008.4712573