Detection of Untrustworthy IoT Measurements Using Expert Knowledge of Their Joint Distribution
The aim of this work is to discuss abnormality detection and explanation challenges motivated by Medical Internet of Things. First, any feature is a measurement taken by a sensor at a time moment, so abnormality detection also becomes a sequential process. Second, an anomaly detection process could not rely on having a large collection of data records, but instead there is a knowledge provided by the experts.
KeywordsAnomaly explanation Untrustworthy data Internet of Things
This work was supported by Technology Integrated Health Management (TIHM) project awarded to the School of Mathematics and Information Security at Royal Holloway as part of an initiative by NHS England supported by InnovateUK, by European Union grant 671555 (“ExCAPE"), and AstraZeneca grant R10911.
- 1.Micenkova, B., Ng, R.T., Dang, X.-H., Assent, I.: Explaining outliers by subspace separability. In: Data Mining (ICDM), pp. 518–527. IEEE (2013)Google Scholar
- 2.Siddiqui, M.A., Fern, A., Dietterich, Th.G., Wong, W.-K.: Sequential Feature Explanations for Anomaly Detection. arXiv:1503.00038 [cs.AI] (2015)
- 5.Zagorecki, A., Orzechowski, P., Holownia, K.: A system for automated general medical diagnosis using Bayesian networks. In: Lehmann, C.U., et al. (eds.) MEDINFO (2013). https://doi.org/10.3233/978-1-61499-289-9-461