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Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection. arXiv preprint arXiv:1801.03168 (2018)."},{"key":"e_1_3_2_2_24_1","volume-title":"USENIX Annual Technical Conference. 1--14","author":"Lou Jian-Guang","year":"2010","unstructured":"Jian-Guang Lou , Qiang Fu , Shengqi Yang , Ye Xu , and Jiang Li . 2010 . Mining Invariants from Console Logs for System Problem Detection .. In USENIX Annual Technical Conference. 1--14 . Jian-Guang Lou, Qiang Fu, Shengqi Yang, Ye Xu, and Jiang Li. 2010. Mining Invariants from Console Logs for System Problem Detection.. In USENIX Annual Technical Conference. 1--14."},{"key":"e_1_3_2_2_25_1","volume-title":"Proceedings. Presses universitaires de Louvain, 89","author":"Malhotra Pankaj","year":"2015","unstructured":"Pankaj Malhotra , Lovekesh Vig , Gautam Shroff , and Puneet Agarwal . 2015 . Long short term memory networks for anomaly detection in time series . In Proceedings. Presses universitaires de Louvain, 89 . 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Learning representations by back-propagating errors. Cognitive modeling Vol. 5 3 (1988) 1. David E Rumelhart Geoffrey E Hinton Ronald J Williams et al. 1988. Learning representations by back-propagating errors. Cognitive modeling Vol. 5 3 (1988) 1."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3229329.3229332"},{"key":"e_1_3_2_2_33_1","volume-title":"Overcoming catastrophic forgetting with hard attention to the task. arXiv preprint arXiv:1801.01423","author":"Serr\u00e0 Joan","year":"2018","unstructured":"Joan Serr\u00e0 , Didac Suris , Marius Miron , and Alexandros Karatzoglou . 2018. Overcoming catastrophic forgetting with hard attention to the task. arXiv preprint arXiv:1801.01423 ( 2018 ). Joan Serr\u00e0, Didac Suris, Marius Miron, and Alexandros Karatzoglou. 2018. 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Credit Card Fraud Detection using Autoencoders in Keras. https:\/\/github.com\/curiousily\/Credit-Card-Fraud-Detection-using-Autoencoders-in-Keras\/blob\/master\/fraud_detection.ipynb [Online; accessed 19-April-2019]."},{"key":"e_1_3_2_2_39_1","volume-title":"Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks","author":"Wang Bolun","unstructured":"Bolun Wang , Yuanshun Yao , Shawn Shan , Huiying Li , Bimal Viswanath , Haitao Zheng , and Ben Y Zhao . [n.d.]. Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks . In Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks . IEEE , 0. Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao. [n.d.]. Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks. In Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks. IEEE, 0."},{"key":"e_1_3_2_2_40_1","unstructured":"Wei Xu. 2009. HDFS Log Dataset. http:\/\/iiis.tsinghua.edu.cn\/ weixu\/sospdata.html [Online; accessed 19-April-2019]. Wei Xu. 2009. HDFS Log Dataset. http:\/\/iiis.tsinghua.edu.cn\/ weixu\/sospdata.html [Online; accessed 19-April-2019]."},{"key":"e_1_3_2_2_41_1","volume-title":"The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=F1_score&oldid=911716685. [Online","author":"Wikipedia","year":"2019","unstructured":"Wikipedia contributors. 2019 a. F1 score -- Wikipedia , The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=F1_score&oldid=911716685. [Online ; accessed 31- August - 2019 ]. Wikipedia contributors. 2019 a. F1 score -- Wikipedia, The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=F1_score&oldid=911716685. [Online; accessed 31-August-2019]."},{"key":"e_1_3_2_2_42_1","volume-title":"The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=Zero-day_(computing)&oldid=895202836. [Online","author":"Wikipedia","year":"2019","unstructured":"Wikipedia contributors. 2019 b. Zero-day (computing) -- Wikipedia , The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=Zero-day_(computing)&oldid=895202836. [Online ; accessed 16- May - 2019 ]. Wikipedia contributors. 2019 b. Zero-day (computing) -- Wikipedia, The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=Zero-day_(computing)&oldid=895202836. [Online; accessed 16-May-2019]."},{"key":"e_1_3_2_2_43_1","unstructured":"Rui Xu and Donald C Wunsch. 2005. Survey of clustering algorithms. (2005). Rui Xu and Donald C Wunsch. 2005. Survey of clustering algorithms. (2005)."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/1629575.1629587"},{"key":"e_1_3_2_2_45_1","volume-title":"A Benchmark Dataset for Time Series Anomaly Detection. https:\/\/yahooresearch.tumblr.com\/post\/114590420346\/a-benchmark-dataset-for-time-series-anomaly [Online","author":"Research Yahoo","year":"2019","unstructured":"Yahoo Research . 2015. A Benchmark Dataset for Time Series Anomaly Detection. https:\/\/yahooresearch.tumblr.com\/post\/114590420346\/a-benchmark-dataset-for-time-series-anomaly [Online ; accessed 19- April - 2019 ]. Yahoo Research. 2015. A Benchmark Dataset for Time Series Anomaly Detection. https:\/\/yahooresearch.tumblr.com\/post\/114590420346\/a-benchmark-dataset-for-time-series-anomaly [Online; accessed 19-April-2019]."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840733"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098052"},{"key":"e_1_3_2_2_48_1","volume-title":"Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen.","author":"Zong Bo","year":"2018","unstructured":"Bo Zong , Qi Song , Martin Renqiang Min , Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018 . Deep autoencoding gaussian mixture model for unsupervised anomaly detection. (2018). Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. 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