A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System
Published in Proceedings of the VLDB Endowment (VLDB), 2022
Anomaly detection is a critical task in applications like preventing financial fraud, system malfunctions, and cybersecurity attacks. While previous research has offered a plethora of anomaly detection algorithms, effective anomaly detection remains challenging for users due to the tedious manual tuning process. Currently, model developers must determine which of these numerous algorithms is best suited for their particular domain and then must tune many parameters by hand to make the chosen algorithm perform well. This demonstration showcases AutoOD, the first unsupervised self-tuning anomaly detection system which frees users from this tedious manual tuning process. AutoOD outperforms the best un-supervised anomaly detection methods it deploys, with its performance similar to those of supervised anomaly classification models, yet without requiring ground truth labels. Our easy-to-use visual interface allows users to gain insights into AutoOD’s self-tuning process and explore the underlying patterns within their datasets.
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Presentation by Dennis Hofmann at VLDB 2022
Recommended citation: Dennis Hofmann, Peter VanNostrand, Huayi Zhang, Yizhou Yan, Lei Cao, Samuel Madden, and Elke Rundensteiner. 2022. A demonstration of AutoOD: a self-tuning anomaly detection system. Proc. VLDB Endow. 15, 12 (August 2022), 3706–3709. https://doi.org/10.14778/3554821.3554880
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