{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T22:18:59Z","timestamp":1725747539118},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,9]]},"DOI":"10.1109\/issre59848.2023.00084","type":"proceedings-article","created":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T17:47:34Z","timestamp":1698947254000},"page":"183-194","source":"Crossref","is-referenced-by-count":0,"title":["fKPISelect: Fault-Injection Based Automated KPI Selection for Practical Multivariate Anomaly Detection"],"prefix":"10.1109","author":[{"given":"Xingjian","family":"Zhang","sequence":"first","affiliation":[{"name":"Tsinghua University,REASONS Lab, Institute for Network Sciences and Cyberspace"}]},{"given":"Yinqin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Tsinghua University,REASONS Lab, Institute for Network Sciences and Cyberspace"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University,REASONS Lab, Institute for Network Sciences and Cyberspace"}]},{"given":"Long","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University,REASONS Lab, Institute for Network Sciences and Cyberspace"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"China Telecom Cloud Technology Co., Ltd."}]},{"given":"Yefei","family":"Hou","sequence":"additional","affiliation":[{"name":"China Telecom Cloud Technology Co., Ltd."}]},{"given":"Zhongwen","family":"Lan","sequence":"additional","affiliation":[{"name":"China Telecom Cloud Technology Co., Ltd."}]},{"given":"Xining","family":"Hu","sequence":"additional","affiliation":[{"name":"China Telecom Cloud Technology Co., Ltd."}]},{"given":"Beibei","family":"Miao","sequence":"additional","affiliation":[{"name":"China Telecom Cloud Technology Co., Ltd."}]},{"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"China Telecom Cloud Technology Co., Ltd."}]},{"given":"Xiangyi","family":"Jing","sequence":"additional","affiliation":[{"name":"China Telecom Cloud Technology Co., Ltd."}]},{"given":"Sijie","family":"Li","sequence":"additional","affiliation":[{"name":"China Telecom Cloud Technology Co., Ltd."}]}],"member":"263","reference":[{"article-title":"Lloyd\u2019s estimates the impact of a u.s. cloud outage at $19 billion","year":"2018","author":"Jackson","key":"ref1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CLOUD.2012.53"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/app112311353"},{"author":"Malhotra","key":"ref4","article-title":"LSTM-based encoder-decoder for multi-sensor anomaly detection"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403392"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450013"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467075"},{"key":"ref9","article-title":"Anomaly transformer: Time series anomaly detection with association discrepancy","volume-title":"International Conference on Learning Representations","author":"Xu","year":"2022"},{"article-title":"Netmanaiops\/ctf data: Data of paper \u201dctf: Anomaly detection in high-dimensional time series with coarse-to-fine model transfer","year":"2021","author":"Authors","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE55969.2022.00014"},{"article-title":"Multivariate time-series anomaly detection via graph attention network","author":"Zhao","key":"ref12","doi-asserted-by":"crossref","DOI":"10.1109\/ICDM50108.2020.00093"},{"article-title":"A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data","author":"Zhang","key":"ref13","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v33i01.33011409"},{"article-title":"Overview \u2014 prometheus","year":"2021","author":"Authors","key":"ref14"},{"article-title":"Grafana \u2014 query, visualize, alerting observability platform","year":"2021","author":"Labs","key":"ref15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2980749"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105827"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2010.25"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE.2019.00014"},{"article-title":"prometheus\/node exporter: Exporter for machine metrics","year":"2021","author":"Authors","key":"ref21"},{"article-title":"Node exporter full \u2014 grafana labs","year":"2021","author":"Labs","key":"ref22"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IWQoS.2018.8624168"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2841425"},{"volume-title":"Precision and Recall for Time Series","author":"Tatbul","key":"ref25"},{"article-title":"Chapter 27. linux traffic control red hat enterprise linux 9","year":"2021","author":"Hat","key":"ref26"},{"year":"2021","key":"ref27","article-title":"Kernel\/reference\/stress-ng - ubuntu wiki"},{"volume-title":"Chaos Mesh","key":"ref28","article-title":"Chaos-mesh\/chaos-mesh"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/QRS-C.2017.119"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1249"},{"volume-title":"Red. Ansible is Simple IT Automation","author":"Hat","key":"ref31"},{"year":"2021","key":"ref32","article-title":"Pytorch"},{"year":"2021","key":"ref33","article-title":"Numpy"},{"volume-title":"Production-Grade Container Orchestration. Kubernetes","key":"ref34"},{"volume-title":"Microservices Demo","key":"ref35","article-title":"Sock Shop : A Microservice Demo Application"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE.2016.32"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185996"},{"author":"Lai","key":"ref39","article-title":"Revisiting Time Series Outlier Detection: Definitions and Benchmarks"},{"first-page":"1","article-title":"Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress","author":"Wu","key":"ref40"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/PCCC.2018.8711315"}],"event":{"name":"2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)","start":{"date-parts":[[2023,10,9]]},"location":"Florence, Italy","end":{"date-parts":[[2023,10,12]]}},"container-title":["2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10299935\/10299936\/10301214.pdf?arnumber=10301214","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T04:51:37Z","timestamp":1712811097000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10301214\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,9]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/issre59848.2023.00084","relation":{},"subject":[],"published":{"date-parts":[[2023,10,9]]}}}