AutoOD: A Self-Tuning Anomaly Detection System

AutoOD is a self-tuning anomaly detection system designed to addressthe challenges of method selection and hyper-parameter tuning while remaining unsupervised. AutoOD frees users from the tedious manual tuning process often required for anomaly detection by intelligently identifying high likelihood inliers and outliers. AutoOD features a responsive visual interface allowing for seamless interaction providing the user with insightful knowledge of how AutoOD operates. AutoOD outperforms the best unsupervised anomaly detection methods, yielding results similar to supervised methods that have access to ground truth labels.

This work has been accepted for publication at VLDB 2022 (48th International Conference on Very Large Databases) one of the most prestigious conferences in database systems.Getting Started: First, upload the dataset you are interested in examining. AutoOD currently accepts CSV and ARFF files. Next, select the unsupervised outlier detection methods you want to run AutoOD with, and provide the column names for the id column and label column from your dataset. Lastly, input the minimum and maximum percentage range for the number of outliers you expect are contained within your dataset.This work has been accepted for publication at VLDB 2022 (48th International Conference on Very Large Databases) one of the most prestigious conferences in database systems.

Getting Started:

First, upload the dataset you are interested in examining. AutoOD currently accepts CSV and ARFF files. Next, select the unsupervised outlier detection methods you want to run AutoOD with, and provide the column names for the id column and label column from your dataset. Lastly, input the minimum and maximum percentage range for the number of outliers you expect are contained within your dataset.

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