{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T07:12:05Z","timestamp":1757574725073,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,5,24]]},"DOI":"10.1145\/3433210.3453098","type":"proceedings-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T15:26:39Z","timestamp":1622820399000},"page":"336-348","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Recompose Event Sequences vs. Predict Next Events: A Novel Anomaly Detection Approach for Discrete Event Logs"],"prefix":"10.1145","author":[{"given":"Lun-Pin","family":"Yuan","sequence":"first","affiliation":[{"name":"Pennsylvania State University, State College, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, State College, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sencun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, State College, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Mejbah Alam Justin Gottschlich Nesime Tatbul Javier Turek Timothy Mattson and Abdullah Muzahid. 2017. A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. arxiv: 1709.07536 [cs.SE]  Mejbah Alam Justin Gottschlich Nesime Tatbul Javier Turek Timothy Mattson and Abdullah Muzahid. 2017. A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. arxiv: 1709.07536 [cs.SE]"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105124"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3217871.3217872"},{"key":"e_1_3_2_2_5_1","volume-title":"Aditya Krishna Menon, and Sanjay Chawla","author":"Chalapathy Raghavendra","year":"2017","unstructured":"Raghavendra Chalapathy , Aditya Krishna Menon, and Sanjay Chawla . 2017 . Robust, Deep and Inductive Anomaly Detection. In Machine Learning and Knowledge Discovery in Databases, Michelangelo Ceci, Jaakko Hollm\u00e9n, Ljupvc o Todorovski, Celine Vens, and Savs o Dvz eroski (Eds.). Springer International Publishing , Cham, 36--51. Raghavendra Chalapathy, Aditya Krishna Menon, and Sanjay Chawla. 2017. Robust, Deep and Inductive Anomaly Detection. In Machine Learning and Knowledge Discovery in Databases, Michelangelo Ceci, Jaakko Hollm\u00e9n, Ljupvc o Todorovski, Celine Vens, and Savs o Dvz eroski (Eds.). Springer International Publishing, Cham, 36--51."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2018.01.023"},{"key":"e_1_3_2_2_7_1","volume-title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. CoRR","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho , Bart van Merrienboer , Caglar G\u00fclcehre , Fethi Bougares , Holger Schwenk , and Yoshua Bengio . 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. CoRR , Vol. abs\/ 1406 .1078 ( 2014 ). arxiv: 1406.1078 Kyunghyun Cho, Bart van Merrienboer, Caglar G\u00fclcehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. CoRR, Vol. abs\/1406.1078 (2014). arxiv: 1406.1078"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363226"},{"volume-title":"Spell: Streaming Parsing of System Event Logs. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 859--864","author":"Du M.","key":"e_1_3_2_2_9_1","unstructured":"M. Du and F. Li . 2016 . Spell: Streaming Parsing of System Event Logs. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 859--864 . M. Du and F. Li. 2016. Spell: Streaming Parsing of System Event Logs. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 859--864."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134015"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"M. O. Ezeme Q. H. Mahmoud and A. Azim. 2018. Hierarchical Attention-Based Anomaly Detection Model for Embedded Operating Systems. In 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). 225--231.  M. O. Ezeme Q. H. Mahmoud and A. Azim. 2018. Hierarchical Attention-Based Anomaly Detection Model for Embedded Operating Systems. In 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). 225--231.","DOI":"10.1109\/RTCSA.2018.00035"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2897122"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3297280.3297314"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Alex Graves Abdelrahman Mohamed and Geoffrey Hinton. 2013. Speech Recognition with Deep Recurrent Neural Networks. arxiv: 1303.5778 [cs.NE]  Alex Graves Abdelrahman Mohamed and Geoffrey Hinton. 2013. Speech Recognition with Deep Recurrent Neural Networks. arxiv: 1303.5778 [cs.NE]","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_3_2_2_15_1","volume-title":"Jan Koutn'i k, Bas R. Steunebrink, and J\u00fcrgen Schmidhuber.","author":"Greff Klaus","year":"2015","unstructured":"Klaus Greff , Rupesh Kumar Srivastava , Jan Koutn'i k, Bas R. Steunebrink, and J\u00fcrgen Schmidhuber. 2015 . LSTM : A Search Space Odyssey. CoRR , Vol. abs\/ 1503 .04069 (2015). arxiv: 1503.04069 Klaus Greff, Rupesh Kumar Srivastava, Jan Koutn'i k, Bas R. Steunebrink, and J\u00fcrgen Schmidhuber. 2015. LSTM: A Search Space Odyssey. CoRR, Vol. abs\/1503.04069 (2015). arxiv: 1503.04069"},{"volume-title":"Advances in Neural Information Processing Systems 26","author":"Hermans Michiel","key":"e_1_3_2_2_16_1","unstructured":"Michiel Hermans and Benjamin Schrauwen . 2013. Training and Analysing Deep Recurrent Neural Networks . In Advances in Neural Information Processing Systems 26 , C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger (Eds.). Curran Associates, Inc. , 190--198. Michiel Hermans and Benjamin Schrauwen. 2013. Training and Analysing Deep Recurrent Neural Networks. In Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 190--198."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"volume-title":"Anomalous User Activity Detection in Enterprise Multi-source Logs. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). 797--803","author":"Hu Q.","key":"e_1_3_2_2_18_1","unstructured":"Q. Hu , B. Tang , and D. Lin . 2017 . Anomalous User Activity Detection in Enterprise Multi-source Logs. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). 797--803 . Q. Hu, B. Tang, and D. Lin. 2017. Anomalous User Activity Detection in Enterprise Multi-source Logs. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). 797--803."},{"key":"e_1_3_2_2_19_1","volume-title":"International conference on machine learning. 2342--2350","author":"Jozefowicz Rafal","year":"2015","unstructured":"Rafal Jozefowicz , Wojciech Zaremba , and Ilya Sutskever . 2015 . An empirical exploration of recurrent network architectures . In International conference on machine learning. 2342--2350 . Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever. 2015. An empirical exploration of recurrent network architectures. In International conference on machine learning. 2342--2350."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3167132.3167180"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363224"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2957055"},{"volume-title":"Olivier De Vel, and Yang Xiang. 2019 a. Unsupervised Insider Detection Through Neural Feature Learning and Model Optimisation","author":"Liu Liu","key":"e_1_3_2_2_23_1","unstructured":"Liu Liu , Chao Chen , Jun Zhang , Olivier De Vel, and Yang Xiang. 2019 a. Unsupervised Insider Detection Through Neural Feature Learning and Model Optimisation . In Network and System Security, Joseph K. Liu and Xinyi Huang (Eds.). Springer International Publishing , Cham , 18--36. Liu Liu, Chao Chen, Jun Zhang, Olivier De Vel, and Yang Xiang. 2019 a. Unsupervised Insider Detection Through Neural Feature Learning and Model Optimisation. In Network and System Security, Joseph K. Liu and Xinyi Huang (Eds.). Springer International Publishing, Cham, 18--36."},{"volume-title":"Anomaly-Based Insider Threat Detection Using Deep Autoencoders. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). 39--48","author":"Liu L.","key":"e_1_3_2_2_24_1","unstructured":"L. Liu , O. De Vel , C. Chen , J. Zhang , and Y. Xiang . 2018a . Anomaly-Based Insider Threat Detection Using Deep Autoencoders. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). 39--48 . L. Liu, O. De Vel, C. Chen, J. Zhang, and Y. Xiang. 2018a. Anomaly-Based Insider Threat Detection Using Deep Autoencoders. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). 39--48."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2843336"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2944419"},{"volume-title":"Proceedings of the 10th International Conference on Recent Advances in Intrusion Detection","author":"Marcus","key":"e_1_3_2_2_27_1","unstructured":"Marcus A. Maloof and Gregory D. Stephens. 2007. ELICIT: A System for Detecting Insiders Who Violate Need-to-Know . In Proceedings of the 10th International Conference on Recent Advances in Intrusion Detection ( Gold Goast, Australia) (RAID'07). Springer-Verlag, Berlin, Heidelberg, 146--166. Marcus A. Maloof and Gregory D. Stephens. 2007. ELICIT: A System for Detecting Insiders Who Violate Need-to-Know. In Proceedings of the 10th International Conference on Recent Advances in Intrusion Detection (Gold Goast, Australia) (RAID'07). Springer-Verlag, Berlin, Heidelberg, 146--166."},{"key":"e_1_3_2_2_28_1","volume-title":"Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. arxiv","author":"Mirsky Yisroel","year":"2018","unstructured":"Yisroel Mirsky , Tomer Doitshman , Yuval Elovici , and Asaf Shabtai . 2018 . Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. arxiv : 1802.09089 [cs.CR] Yisroel Mirsky, Tomer Doitshman, Yuval Elovici, and Asaf Shabtai. 2018. Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. arxiv: 1802.09089 [cs.CR]"},{"volume-title":"2015 Military Communications and Information Systems Conference (MilCIS). 1--6.","author":"Moustafa N.","key":"e_1_3_2_2_29_1","unstructured":"N. Moustafa and J. Slay . 2015. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) . In 2015 Military Communications and Information Systems Conference (MilCIS). 1--6. N. Moustafa and J. Slay. 2015. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In 2015 Military Communications and Information Systems Conference (MilCIS). 1--6."},{"volume-title":"GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection. In 2019 IEEE Conference on Communications and Network Security (CNS). 91--99","author":"Nguyen Q. P.","key":"e_1_3_2_2_30_1","unstructured":"Q. P. Nguyen , K. W. Lim , D. M. Divakaran , K. H. Low , and M. C. Chan . 2019 . GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection. In 2019 IEEE Conference on Communications and Network Security (CNS). 91--99 . Q. P. Nguyen, K. W. Lim, D. M. Divakaran, K. H. Low, and M. C. Chan. 2019. GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection. In 2019 IEEE Conference on Communications and Network Security (CNS). 91--99."},{"volume-title":"2015 45th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks. 45--56","author":"Oprea A.","key":"e_1_3_2_2_31_1","unstructured":"A. Oprea , Z. Li , T. Yen , S. H. Chin , and S. Alrwais . 2015. Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data . In 2015 45th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks. 45--56 . A. Oprea, Z. Li, T. Yen, S. H. Chin, and S. Alrwais. 2015. Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data. In 2015 45th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks. 45--56."},{"key":"e_1_3_2_2_32_1","unstructured":"Razvan Pascanu Caglar Gulcehre Kyunghyun Cho and Yoshua Bengio. 2013. How to Construct Deep Recurrent Neural Networks. arxiv: 1312.6026 [cs.NE]  Razvan Pascanu Caglar Gulcehre Kyunghyun Cho and Yoshua Bengio. 2013. How to Construct Deep Recurrent Neural Networks. arxiv: 1312.6026 [cs.NE]"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1010"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2017.2772792"},{"key":"e_1_3_2_2_36_1","volume-title":"Unsupervised Learning of Video Representations using LSTMs. CoRR","author":"Srivastava Nitish","year":"2015","unstructured":"Nitish Srivastava , Elman Mansimov , and Ruslan Salakhutdinov . 2015. Unsupervised Learning of Video Representations using LSTMs. CoRR , Vol. abs\/ 1502 .04681 ( 2015 ). arxiv: 1502.04681 Nitish Srivastava, Elman Mansimov, and Ruslan Salakhutdinov. 2015. Unsupervised Learning of Video Representations using LSTMs. CoRR, Vol. abs\/1502.04681 (2015). arxiv: 1502.04681"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105187"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2009.19"},{"volume-title":"Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles","author":"Xu Wei","key":"e_1_3_2_2_39_1","unstructured":"Wei Xu , Ling Huang , Armando Fox , David Patterson , and Michael I. Jordan . 2009b. Detecting Large-Scale System Problems by Mining Console Logs . In Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles ( Big Sky, Montana, USA) (SOSP '09). Association for Computing Machinery, New York, NY, USA, 117--132. Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael I. Jordan. 2009b. Detecting Large-Scale System Problems by Mining Console Logs. In Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles (Big Sky, Montana, USA) (SOSP '09). Association for Computing Machinery, New York, NY, USA, 117--132."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2953981"},{"volume-title":"2017 International Joint Conference on Neural Networks (IJCNN). 3854--3861","author":"Yousefi-Azar M.","key":"e_1_3_2_2_41_1","unstructured":"M. Yousefi-Azar , V. Varadharajan , L. Hamey , and U. Tupakula . 2017. Autoencoder-based feature learning for cyber security applications . In 2017 International Joint Conference on Neural Networks (IJCNN). 3854--3861 . M. Yousefi-Azar, V. Varadharajan, L. Hamey, and U. Tupakula. 2017. Autoencoder-based feature learning for cyber security applications. In 2017 International Joint Conference on Neural Networks (IJCNN). 3854--3861."},{"volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD '17)","author":"Zhou Chong","key":"e_1_3_2_2_42_1","unstructured":"Chong Zhou and Randy C. Paffenroth . 2017. Anomaly Detection with Robust Deep Autoencoders . In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD '17) . Association for Computing Machinery, New York, NY, USA, 665--674. Chong Zhou and Randy C. Paffenroth. 2017. Anomaly Detection with Robust Deep Autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD '17). Association for Computing Machinery, New York, NY, USA, 665--674."},{"key":"e_1_3_2_2_43_1","volume-title":"Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. In International Conference on Learning Representations.","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. In International Conference on Learning Representations. 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. In International Conference on Learning Representations."}],"event":{"name":"ASIA CCS '21: ACM Asia Conference on Computer and Communications Security","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"],"location":"Virtual Event Hong Kong","acronym":"ASIA CCS '21"},"container-title":["Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3433210.3453098","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3433210.3453098","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:12Z","timestamp":1750193292000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3433210.3453098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,24]]},"references-count":43,"alternative-id":["10.1145\/3433210.3453098","10.1145\/3433210"],"URL":"https:\/\/doi.org\/10.1145\/3433210.3453098","relation":{},"subject":[],"published":{"date-parts":[[2021,5,24]]},"assertion":[{"value":"2021-06-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}