{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T06:13:24Z","timestamp":1777184004946,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819708079","type":"print"},{"value":"9789819708086","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-0808-6_24","type":"book-chapter","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T16:02:20Z","timestamp":1708963340000},"page":"407-427","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Log Anomaly Detection Based on\u00a0Semantic Features and\u00a0Topic Features"],"prefix":"10.1007","author":[{"given":"Peipeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuguo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiying","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"He, S., He, P.J., Chen, Z.B., Yang, T.Y., Su, Y.X., Lyu, M.R.: A survey on automated log analysis for reliability engineering. ACM Comput. Surv. 54(6), 1\u201337 (2022)","DOI":"10.1145\/3460345"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Le, V., Zhang, H.Y.: Log-based anomaly detection with deep learning: how far are we? In: 44th IEEE\/ACM 44th International Conference on Software Engineering (ICSE), pp. 1356-1367(2022)","DOI":"10.1145\/3510003.3510155"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"He, P.J., Zhu, J.M., He, S.L., Li, J., Lyu, M. R.: Towards automated log parsing for large-scale log data analysis. IEEE Trans. Depend. Secure Comput. 15(6), 931\u2013944(2018)","DOI":"10.1109\/TDSC.2017.2762673"},{"key":"24_CR4","unstructured":"Xu, W., Huang, L., Fox, A., Patterson, D.A., Jordan, M.I.: Detecting large-scale system problems by mining console logs. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 37\u201346 (2010)"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Chen, M.Y., Zheng, A.X., Lloyd, J., Jordan, M.I., Brewer, E.A.: Failure diagnosis using decision trees. In: 1st International Conference on Autonomic Computing, pp. 36\u201343 (2004)","DOI":"10.1109\/ICAC.2004.1301345"},{"key":"24_CR6","unstructured":"Lou, J.-G., Fu, Q., Yang, S.Q., Xu,Y., and Li , J.: Mining invariants from console logs for system problem detection. In: USENIX Annual Technical Conference (ATC), pp. 1\u201314 (2010)"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Du, M., Li, F., Zheng, G., Srikumar, V.: DeepLog: anomaly detection and diagnosis from system logs through deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1285\u20131298 (2017)","DOI":"10.1145\/3133956.3134015"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Wang, J. et al.: LogEvent2vec: LogEvent-To-vector based anomaly detection for large-scale logs in internet of things. Sensors 20(9) (2020)","DOI":"10.3390\/s20092451"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Robust log-based anomaly detection on unstable log data. In: Proceedings of 27th ACM Joint Meeting Eur. Softw. Eng. Conf. Symp. Foundations Softw. Eng., pp. 807\u2013817 (2019)","DOI":"10.1145\/3338906.3338931"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Wang, Z.M., Tian, J.Y., Fang, H., Chen, L.M., Qin, J.: LightLog: a lightweight temporal convolutional network for log anomaly detection on the edge. Comput. Networks 203 (2022)","DOI":"10.1016\/j.comnet.2021.108616"},{"key":"24_CR11","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova., K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, Minneapolis, pp. 4171\u20134186 (2019)"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Akritidis, L., Bozanis, P.: How dimensionality reduction affects sentiment analysis NLP tasks: an experimental study. In: 18th AIAI, pp. 301\u2013312 (2022)","DOI":"10.1007\/978-3-031-08337-2_25"},{"issue":"4\u20135","key":"24_CR13","first-page":"993","volume":"3","author":"M David","year":"2003","unstructured":"David, M., Andrew, Y., Michael, I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4\u20135), 993\u20131022 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR14","unstructured":"Bai, S.J., Kolter, J. Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling (2018)"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Makanju, A., Zincir-Heywood, A., Milios, E.: A lightweight algorithm for message type extraction in system application logs. IEEE Trans. Know. Data Eng. 24(11), 1921\u20131936 (2012)","DOI":"10.1109\/TKDE.2011.138"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Du, M., Li, F.: Spell: streaming parsing of system event logs. In: IEEE 16th International Conference on Data Mining (ICDM), pp. 859\u2013864 (2016)","DOI":"10.1109\/ICDM.2016.0103"},{"issue":"3","key":"24_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3477539","volume":"15","author":"Y Shi","year":"2021","unstructured":"Shi, Y., et al.: An improved KNN-based efficient log anomaly detection method with automatically labeled samples. ACM Trans. Knowl. Discov. Data 15(3), 1\u201322 (2021)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Li, X.Y., Chen, P.F., Jing, L.X., He, Z.L., Yu, G.B.: SwissLog: robust and unified deep learning based log anomaly detection for diverse faults. In: 31st IEEE International Symposium on Software Reliability Engineering (ISSRW), pp. 92\u2013103 (2020)","DOI":"10.1109\/ISSRE5003.2020.00018"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Fu, Q., Lou, J.G., Wang, Y., Li, J.: Execution anomaly detection in distributed systems through unstructured log analysis. In: The Ninth IEEE International Conference on Data Mining (ICDM), pp. 149\u2013158 (2009)","DOI":"10.1109\/ICDM.2009.60"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Messaoudi, S., Panichella, A., Bianculli, D., Biand, L.C., Sasnauakas R.: A search-based approach for accurate identification of log message formats. In: Proceedings of the 26th Conference on Program Comprehension (ICPC), pp. 167\u201316710 (2018)","DOI":"10.1145\/3196321.3196340"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, J.M. et al.: Tools and benchmarks for automated log parsing. In: Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice, pp. 121\u2013130 (2019)","DOI":"10.1109\/ICSE-SEIP.2019.00021"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"He, P., Zhu, J.M, Zheng, Z.B., Lyu, M. R.: Drain: an online log parsing approach with fixed depth tree. In: IEEE International Conference on Web Services (ICWS), pp. 33\u201340 (2017)","DOI":"10.1109\/ICWS.2017.13"},{"issue":"4","key":"24_CR23","doi-asserted-by":"publisher","first-page":"4119","DOI":"10.1109\/TNSM.2021.3125967","volume":"18","author":"CK Zhang","year":"2021","unstructured":"Zhang, C.K., Wang, X.Y., Zhang, H.Y., Zhang, H.Y., Han, P.Y.: Log sequence anomaly detection based on local information extraction and globally sparse transformer model. IEEE Trans. Netw. Serv. Man. 18(4), 4119\u20134133 (2021)","journal-title":"IEEE Trans. Netw. Serv. Man."},{"key":"24_CR24","unstructured":"Lin, Y., et al.: Semi-supervised log-based anomaly detection via probabilistic label estimation. In: 43rd IEEE\/ACM International Conference on Software Engineering: Companion Proceedings (ICSE), pp. 230-231 (2021)"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Han, X., Yuan, S.: Unsupervised cross-system log anomaly detection via domain adaptation. In: The 30th ACM International Conference on Information and Knowledge Management (CIKM), pp. 3068\u20133072 (2021)","DOI":"10.1145\/3459637.3482209"},{"key":"24_CR26","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.future.2021.05.024","volume":"124","author":"X Liu","year":"2021","unstructured":"Liu, X., et al.: LogNADS: network anomaly detection scheme based on log semantics representation. Future Gener. Comp. Sy. 124, 390\u2013405 (2021)","journal-title":"Future Gener. Comp. Sy."},{"key":"24_CR27","doi-asserted-by":"crossref","unstructured":"Chen, R. et al.: LogTransfer: cross-system log anomaly detection for software systems with transfer learning. In: 31st IEEE International Symposium on Software Reliability Engineering (ISSRE), pp. 37\u201347(2020)","DOI":"10.1109\/ISSRE5003.2020.00013"},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Q.Z., Zhang, X.G., Wang, X.J., Cao, Z.Y.: Log sequence anomaly detection method based on contrastive adversarial training and dual feature extraction. Entropy 24(1) (2022)","DOI":"10.3390\/e24010069"},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Meng, W.B., et al.: LogAnomaly: unsupervised detection of sequential and quantitative anomalies in unstructured logs. In: Proceedings of the Twenty Eighth International Joint Conference on Artificial Intelligence (IJCAI), pp. 4739\u20134745 (2019)","DOI":"10.24963\/ijcai.2019\/658"},{"key":"24_CR30","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1109\/TIFS.2022.3201379","volume":"17","author":"JW Zhou","year":"2022","unstructured":"Zhou, J.W., Qian, Y.J., Zou, Q.T., Liu, P., Xiang, J.W.: DeepSyslog: deep anomaly detection on syslog using sentence embedding and metadata. IEEE Trans. Inf. Foren. Sec. 17, 3051\u20133061 (2022)","journal-title":"IEEE Trans. Inf. Foren. Sec."},{"issue":"4","key":"24_CR31","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1109\/TNSM.2020.3034647","volume":"17","author":"SH Huang","year":"2020","unstructured":"Huang, S.H., et al.: HitAnomaly: hierarchical transformers for anomaly detection in system log. IEEE Trans. Netw. Serv. Man. 17(4), 2064\u20132076 (2020)","journal-title":"IEEE Trans. Netw. Serv. Man."},{"issue":"3","key":"24_CR32","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.32604\/csse.2022.022365","volume":"41","author":"J Wang","year":"2022","unstructured":"Wang, J., Zhao, C.Q., He, S.M., Gu, Y., Alfarraj, O., Abugabah, A.: LogUAD: log unsupervised anomaly detection based on Word2Vec. Comput. Syst. Sci. Eng. 41(3), 1207\u20131222 (2022)","journal-title":"Comput. Syst. Sci. Eng."},{"key":"24_CR33","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Annual Conference on Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Peinelt, N., Dong, N., Maria, L.: tBERT: topic models and BERT joining forces for semantic similarity detection. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics(ACL), pp. 7047\u20137055 (2020)","DOI":"10.18653\/v1\/2020.acl-main.630"},{"key":"24_CR35","unstructured":"He, S.L., Zhu, J.M., He, P.J., Lyu, M.R.: Loghub: a large collection of system log datasets towards automated log analytics (2020)"},{"key":"24_CR36","unstructured":"Diederik, P., Jimmy, B.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR) (2015)"}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-0808-6_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T22:10:48Z","timestamp":1731449448000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-0808-6_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819708079","9789819708086"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-0808-6_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tjutanklab.com\/ica3pp2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Online submission system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"439","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"145","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}