Online anomaly detection with bandwidth optimized hierarchical Kernel density estimators

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Kerpiççi, Mine and Özkan, Hüseyin and Kozat, Süleyman Serdar (2020) Online anomaly detection with bandwidth optimized hierarchical Kernel density estimators. IEEE Transactions on Neural Networks and Learning Systems . ISSN 2162-237X (Print) 2162-2388 (Online) Published Online First

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We propose a novel unsupervised anomaly detection algorithm that can work for sequential data from any complex distribution in a truly online framework with mathematically proven strong performance guarantees. First, a partitioning tree is constructed to generate a doubly exponentially large hierarchical class of observation space partitions, and every partition region trains an online kernel density estimator (KDE) with its own unique dynamical bandwidth. At each time, the proposed algorithm optimally combines the class estimators to sequentially produce the final density estimation. We mathematically prove that the proposed algorithm learns the optimal partition with kernel bandwidths that are optimized in both region-specific and time-varying manner. The estimated density is then compared with a data-adaptive threshold to detect anomalies. Overall, the computational complexity is only linear in both the tree depth and data length. In our experiments, we observe significant improvements in anomaly detection accuracy compared with the state-of-the-art techniques.
Item Type: Article
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Hüseyin Özkan
Date Deposited: 12 Oct 2020 18:07
Last Modified: 12 Oct 2020 18:07

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