{"type":"ScholarlyArticle","title":"An Anomaly Detection and Explainability Framework using Convolutional Autoencoders for Data Storage Systems","authors":[{"name":"Roy Assaf","slug":"roy-assaf","given":"Roy","family":"Assaf","affiliation":"IBM Research , Zurich"},{"name":"Ioana Giurgiu","slug":"ioana-giurgiu","given":"Ioana","family":"Giurgiu","affiliation":"IBM Research , Zurich"},{"name":"Jonas Pfefferle","slug":"jonas-pfefferle","given":"Jonas","family":"Pfefferle","affiliation":"IBM Research , Zurich"},{"name":"Serge Monney","slug":"serge-monney","given":"Serge","family":"Monney","affiliation":"IBM , Switzerland"},{"name":"Haris Pozidis","slug":"haris-pozidis","given":"Haris","family":"Pozidis","affiliation":"IBM Research , Zurich"},{"name":"Anika Schumann","slug":"anika-schumann","given":"Anika","family":"Schumann","affiliation":"IBM Research , Zurich"}],"abstract":"Anomaly detection in data storage systems is a challenging problem due to the high dimensional sequential data involved, and lack of labels.\r\nThe state of the art for automating anomaly detection in these systems typically relies on hand crafted rules and thresholds which mainly allow to distinguish between normal and abnormal behavior of each indicator in isolation.\r\nIn this work we present an end-to-end framework based on convolutional autoencoders which not only allows for anomaly detection on multivariate time series data, but also provides explainability. This is done by identifying similar historic anomalies and extracting the most influential indicators. These are then presented to relevant personnel such as system designers and architects, or to support engineers for further analysis.\r\nWe demonstrate the application of this framework along with an intuitive interactive web interface which was developed for data storage system anomaly detection. We discuss how this framework along with its explainability aspects enables support engineers to effectively tackle abnormal behaviors, all while allowing for crucial feedback.","doi":"10.24963/ijcai.2020/752","url":"https://ijcai.org/proceedings/2020/752","pdf":"https://ijcai.org/proceedings/2020/0752.pdf","markdown":"https://ijcai.org/proceedings/2020/752.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/752","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Demos","track":"Demos","number":752,"pages":{"first":5228,"last":5230},"keywords":["Machine Learning: general","Human-Computer Interactive Systems: general"],"videos":[],"references":[{"position":1,"raw":"[Assaf et al., 2019] Roy Assaf, Ioana Giurgiu, Frank Bage- horn, and Anika Schumann. 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