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In this article, we propose an online and unsupervised anomaly detection algorithm for streaming data using an array of sliding windows and the probability density-based descriptors (PDDs) (based on these windows). This algorithm mainly consists of three steps: 1) we use a main sliding window over streaming data and segment this window into an array of nonoverlapping subwindows; 2) we propose the PDDs with dimension reduction, based on the kernel density estimation, to estimate the probability density of data in each subwindow; and 3) we design the distance-based anomaly detection rule to determine whether the current observation is anomalous. The experimental results and performances are presented based on the Numenta anomaly benchmark. Compared with the anomaly detection algorithm using the hierarchical temporal memory proposed by Numenta (which outperforms a wide range of other anomaly detection algorithms), our algorithm can perform better in many cases, that is, with higher detection rates and earlier detection for contextual anomalies and concept drifts.
This article was published in the following journal.
Name: IEEE transactions on cybernetics
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A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
Supporting content or information, such as animation, datasets, multimedia files, video, movies, audio files, text files, or software, which is submitted for publication in an online journal or an online edition of a journal. This information may be referenced in the text of the article with a link to the supplementary data provided. CATALOG: do not use
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Information application based on a variety of coding methods to minimize the amount of data to be stored, retrieved, or transmitted. Data compression can be applied to various forms of data, such as images and signals. It is used to reduce costs and increase efficiency in the maintenance of large volumes of data.
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