A Demonstration of AutoOD: A Self-tuning Anomaly Detection System
A Demonstration of AutoOD: A Self-tuning Anomaly Detection System
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DOI：
10.14778/3554821.3554880
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Dennis M. Hofmann;Peter M. VanNostrand;Huayi Zhang;Yizhou Yan;Lei Cao;S. Madden;Elke A. Rundensteiner
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文献类型：
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作者：
Dennis M. Hofmann;Peter M. VanNostrand;Huayi Zhang;Yizhou Yan;Lei Cao;S. Madden;Elke A. Rundensteiner
异常检测是防止金融欺诈、系统故障和网络安全攻击等应用中的一项关键任务。虽然之前的研究提供了大量的异常检测算法，但由于繁琐的手动调整过程，有效的异常检测对用户来说仍然具有挑战性。目前，模型开发人员必须确定众多算法中哪一种最适合其特定领域，然后必须手动调整许多参数以使所选算法表现良好。该演示展示了 AutoOD，这是第一个无监督的自调整异常检测系统，可将用户从繁琐的手动调整过程中解放出来。 AutoOD 的性能优于其部署的最佳无监督异常检测方法，其性能与有监督异常分类模型相似，但不需要真实标签。我们易于使用的可视化界面使用户能够深入了解 AutoOD 的自调整过程并探索数据集中的潜在模式。
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.