{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:28:23Z","timestamp":1783528103549,"version":"3.55.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["No. 202206490011"],"award-info":[{"award-number":["No. 202206490011"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Netw Syst Manage"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10922-025-09912-5","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T16:18:14Z","timestamp":1740068294000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["LogAnomEX: An Unsupervised Log Anomaly Detection Method Based on Electra-DP and Gated Bilinear Neural Networks"],"prefix":"10.1007","volume":"33","author":[{"given":"Keyuan","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqiang","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"key":"9912_CR1","doi-asserted-by":"publisher","unstructured":"Jia, T., Li, Y., Wu, Z.: Survey of state-of-the-art log-based failure diagnosis. J. Softw. 31(07), 1997\u20132018 (2020) (in Chinese). https:\/\/doi.org\/10.13328\/j.cnki.jos.006045","DOI":"10.13328\/j.cnki.jos.006045"},{"key":"9912_CR2","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Dong, J., Xie, L., Wang, Z., Qin, S., Xu, P., Yin, M.: Recurrent multi-view collaborative registration network for 3D reconstruction and optical measurement of blade profiles. Knowl.-Based Syst. 295, 111857. Elsevier(2024)","DOI":"10.1016\/j.knosys.2024.111857"},{"issue":"10","key":"9912_CR3","doi-asserted-by":"publisher","first-page":"1922","DOI":"10.3390\/electronics13101922","volume":"13","author":"K Qiu","year":"2024","unstructured":"Qiu, K., Zhang, Y., Zhao, J., Zhang, S., Wang, Q., Chen, F.: A multimodal sentiment analysis approach based on a joint chained interactive attention mechanism. Electronics 13(10), 1922 (2024). (MDPI)","journal-title":"Electronics"},{"issue":"9","key":"9912_CR4","doi-asserted-by":"publisher","first-page":"667","DOI":"10.3390\/insects15090667","volume":"15","author":"K Qiu","year":"2024","unstructured":"Qiu, K., Zhang, Y., Ren, Z., et al.: SpemNet: a cotton disease and pest identification method based on efficient multi-scale attention and stacking patch embedding. Insects 15(9), 667 (2024)","journal-title":"Insects"},{"key":"9912_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xu, Y., Lin, Q., Qiao, B., Zhang, H., Dang, Y., Xie, C., Yang, X., Cheng, Q., Li, Z., et al.: Robust log-based anomaly detection on unstable log data. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 807\u2013817 (2019)","DOI":"10.1145\/3338906.3338931"},{"issue":"5","key":"9912_CR6","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.1109\/TDSC.2020.3037903","volume":"18","author":"H Studiawan","year":"2020","unstructured":"Studiawan, H., Sohel, F., Payne, C.: Anomaly detection in operating system logs with deep learning-based sentiment analysis. IEEE Trans. Depend. Secur. Comput. 18(5), 2136\u20132148 (2020). (IEEE)","journal-title":"IEEE Trans. Depend. Secur. Comput."},{"key":"9912_CR7","unstructured":"Clark, K., Luong, M.-T., Le, Q.V., Manning, C.D.: Electra: pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)"},{"issue":"08","key":"9912_CR8","first-page":"169","volume":"37","author":"T Wang","year":"2023","unstructured":"Wang, T., Chen, B., Huang, R., Reng, L., Chen, Y., Qin, Y.: Chinese grammatical error diagnosis model based on electra and gated-bilinear neural network. J. Chin. Inf. Process. 37(08), 169\u2013178 (2023). ((in Chinese))","journal-title":"J. Chin. Inf. Process."},{"key":"9912_CR9","doi-asserted-by":"crossref","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR), 41(3), 1\u201358 (2009). ACM, New York","DOI":"10.1145\/1541880.1541882"},{"key":"9912_CR10","doi-asserted-by":"crossref","unstructured":"He, S., Zhu, J., He, P., Lyu, M.R.: Experience report: System log analysis for anomaly detection. In: 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE), pp. 207\u2013218. IEEE (2016)","DOI":"10.1109\/ISSRE.2016.21"},{"key":"9912_CR11","doi-asserted-by":"crossref","unstructured":"Xu, W., Huang, L., Fox, A., Patterson, D., Jordan, M.I.: Detecting large-scale system problems by mining console logs. In: Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles, pp. 117\u2013132 (2009)","DOI":"10.1145\/1629575.1629587"},{"key":"9912_CR12","doi-asserted-by":"crossref","unstructured":"Lin, Q., Zhang, H., Lou, J.-G., Zhang, Y., Chen, X.: Log clustering based problem identification for online service systems. In: Proceedings of the 38th International Conference on Software Engineering Companion, pp. 102\u2013111 (2016)","DOI":"10.1145\/2889160.2889232"},{"key":"9912_CR13","doi-asserted-by":"crossref","unstructured":"Liang, Y., Zhang, Y., Xiong, H., Sahoo, R.: Failure prediction in ibm bluegene\/l event logs. In: 7th IEEE International Conference on Data Mining (ICDM 2007), pp. 583\u2013588. IEEE (2007)","DOI":"10.1109\/ICDM.2007.46"},{"key":"9912_CR14","doi-asserted-by":"crossref","unstructured":"Bodik, P., Goldszmidt, M., Fox, A., Woodard, D.B., Andersen, H.: Fingerprinting the datacenter: automated classification of performance crises. In: Proceedings of the 5th European Conference on Computer Systems, pp. 111\u2013124 (2010)","DOI":"10.1145\/1755913.1755926"},{"key":"9912_CR15","doi-asserted-by":"crossref","unstructured":"Chen, M., Zheng, A.X., Lloyd, J., Jordan, M.I., Brewer, E.: Failure diagnosis using decision trees. In: International Conference on Autonomic Computing, 2004. Proceedings, pp. 36\u201343. IEEE (2004)","DOI":"10.1109\/ICAC.2004.1301345"},{"key":"9912_CR16","doi-asserted-by":"crossref","unstructured":"Qi, J., Luan, Z., Huang, S., Fung, C., Yang, H., Li, H., Zhu, D., Qian, D.: Logencoder: log-based contrastive representation learning for anomaly detection. In: IEEE Transactions on Network and Service Management. IEEE (2023)","DOI":"10.1109\/TNSM.2023.3239522"},{"key":"9912_CR17","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"9912_CR18","doi-asserted-by":"crossref","unstructured":"Salton, G., Buckley, C.: Term Weighting Approaches in Automatic Text Retrieval. Cornell University (1987)","DOI":"10.1016\/0306-4573(88)90021-0"},{"key":"9912_CR19","doi-asserted-by":"crossref","unstructured":"Liu, F., Wen, Y., Zhang, D., Jiang, X., Xing, X., Meng, D.: Log2vec: a heterogeneous graph embedding based approach for detecting cyber threats within enterprise. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 1777\u20131794 (2019)","DOI":"10.1145\/3319535.3363224"},{"key":"9912_CR20","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":"9912_CR21","unstructured":"Sasaki, S., Suzuki, J., Inui, K.: Subword-based compact reconstruction of word embeddings. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 3498\u20133508 (2019)"},{"key":"9912_CR22","doi-asserted-by":"crossref","unstructured":"Wang, J., Yu, L.-C., Lai, K.R., Zhang, X.: Dimensional sentiment analysis using a regional CNN-LSTM model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 225\u2013230 (2016)","DOI":"10.18653\/v1\/P16-2037"},{"key":"9912_CR23","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s00779-018-1183-9","volume":"23","author":"L Luo","year":"2019","unstructured":"Luo, L.: Network text sentiment analysis method combining LDA text representation and GRU-CNN. Pers. Ubiquit. Comput. 23, 405\u2013412 (2019). (Springer)","journal-title":"Pers. Ubiquit. Comput."},{"key":"9912_CR24","doi-asserted-by":"publisher","first-page":"2067","DOI":"10.1016\/S0031-3203(00)00162-X","volume":"34","author":"H Yu","year":"2001","unstructured":"Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data?with application to face recognition. Pattern Recogn. 34, 2067\u20132070 (2001). (Elsevier)","journal-title":"Pattern Recogn."},{"issue":"03","key":"9912_CR25","first-page":"12","volume":"3","author":"S Yan","year":"2024","unstructured":"Yan, S., Shi, F., Yu, K., et al.: TASA: template-driven log anomaly detection with variable integration and sparse attention. Artif. Intell. Secur. 3(03), 12\u201320 (2024)","journal-title":"Artif. Intell. Secur."},{"key":"9912_CR26","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"9912_CR27","unstructured":"Vaswani, A. Attention is all you need. Advances in Neural Information Processing Systems, (2017)."},{"key":"9912_CR28","doi-asserted-by":"crossref","unstructured":"Guo, H., Yuan, S., Wu, X.: Logbert: log anomaly detection via BERT. In: Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9534113"},{"issue":"09","key":"9912_CR29","first-page":"1587","volume":"46","author":"J Yu","year":"2024","unstructured":"Yu, J., Hu, Z., Jiang, C.: Multi-feature-based log event anomaly detection. Comput. Eng. Sci. 46(09), 1587\u20131597 (2024)","journal-title":"Comput. Eng. Sci."},{"key":"9912_CR30","unstructured":"Yamanaka, Y., Takahashi, T., Minami, T., Nakajima, Y.: LogELECTRA: self-supervised anomaly detection for unstructured logs. arXiv preprint arXiv:2402.10397 (2024)"},{"key":"9912_CR31","unstructured":"Lin, Y., Deng, H., Li, X.: FastLogAD: log anomaly detection with mask-guided pseudo anomaly generation and discrimination. arXiv preprint arXiv:2404.08750 (2024)"},{"issue":"8","key":"9912_CR32","doi-asserted-by":"publisher","first-page":"5186","DOI":"10.1109\/TIT.2013.2257913","volume":"59","author":"M Mardani","year":"2013","unstructured":"Mardani, M., Mateos, G., Giannakis, G.B.: Recovery of low-rank plus compressed sparse matrices with application to unveiling traffic anomalies. IEEE Trans. Inf. Theory 59(8), 5186\u20135205 (2013)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"9912_CR33","doi-asserted-by":"crossref","unstructured":"Jin, Y., Qiu, C., Sun, L., et al.: Anomaly detection in time series via robust PCA. In: Proceedings of the 2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE), pp. 352\u2013355. IEEE (2017)","DOI":"10.1109\/ICITE.2017.8056937"},{"issue":"3","key":"9912_CR34","first-page":"665","volume":"53","author":"PS Kalaki","year":"2023","unstructured":"Kalaki, P.S., Shameli-Sendi, A., Abbasi, B.K.E.: Anomaly detection on openstack logs based on an improved robust principal component analysis model and its projection onto column space. Softw.: Pract. Exp. 53(3), 665\u2013681 (2023)","journal-title":"Softw.: Pract. Exp."},{"key":"9912_CR35","doi-asserted-by":"crossref","unstructured":"Weber, I., Garimella, V.R.K., Borra, E.: Mining web query logs to analyze political issues. In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 330\u2013334 (2012)","DOI":"10.1145\/2380718.2380761"},{"key":"9912_CR36","doi-asserted-by":"crossref","unstructured":"Guzman, E., Az\u00f3car, D., Li, Y.: Sentiment analysis of commit comments in GitHub: an empirical study. In: Proceedings of the 11th Working Conference on Mining Software Repositories, pp. 352\u2013355 (2014)","DOI":"10.1145\/2597073.2597118"},{"key":"9912_CR37","doi-asserted-by":"publisher","unstructured":"Dong, Y., Zhao, K.: Log anomaly detection method based on attention mechanism multi-feature fusion and text sentiment analysis . J. Sichuan Univer. (Nat. Sci. Ed.) 61, 76\u201386 (2024) (in Chinese). https:\/\/doi.org\/10.19907\/j.0490-6756.2024.023001","DOI":"10.19907\/j.0490-6756.2024.023001"},{"key":"9912_CR38","doi-asserted-by":"crossref","unstructured":"Meng, W., Liu, Y., Zhu, Y., Zhang, S., Pei, D., Liu, Y., Chen, Y., Zhang, R., Tao, S., Sun, P.: Loganomaly: unsupervised detection of sequential and quantitative anomalies in unstructured logs. In: IJCAI, pp. 4739\u20134745 (2019)","DOI":"10.24963\/ijcai.2019\/658"},{"key":"9912_CR39","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1109\/TKDE.2018.2875442","volume":"31","author":"M Du","year":"2018","unstructured":"Du, M., Li, F.: Spell: online streaming parsing of large unstructured system logs. IEEE Trans. Knowl. Data Eng. 31, 2213\u20132227 (2018). (IEEE)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"9912_CR40","doi-asserted-by":"crossref","unstructured":"Song, X., Salcianu, A., Song, Y., Dopson, D., Zhou, D.: Fast wordpiece tokenization. arXiv preprint arXiv:2012.15524 (2020)","DOI":"10.18653\/v1\/2021.emnlp-main.160"},{"key":"9912_CR41","doi-asserted-by":"crossref","unstructured":"He, P., Zhu, J., Zheng, Z., et al.: Drain: an online log parsing approach with fixed depth tree. In: Proceedings of the 2017 IEEE International Conference on Web Services (ICWS), Honolulu. IEEE (2017)","DOI":"10.1109\/ICWS.2017.13"},{"key":"9912_CR42","doi-asserted-by":"crossref","unstructured":"Hao, Y., Dong, L., Bao, H., Xu, K., Wei, F.: Learning to sample replacements for electra pre-training. arXiv preprint arXiv:2106.13715 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.394"},{"key":"9912_CR43","doi-asserted-by":"crossref","unstructured":"Nedelkoski, S., Bogatinovski, J., Acker, A., Cardoso, J., Kao, O.: Self-attentive classification-based anomaly detection in unstructured logs. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 1196\u20131201. IEEE (2020)","DOI":"10.1109\/ICDM50108.2020.00148"},{"key":"9912_CR44","doi-asserted-by":"crossref","unstructured":"Oliner, A., Stearley, J.: What supercomputers say: A study of five system logs. In: 37th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN\u201907), pp. 575\u2013584. IEEE (2007)","DOI":"10.1109\/DSN.2007.103"},{"key":"9912_CR45","doi-asserted-by":"crossref","unstructured":"Zhu, J., He, S., He, P., Liu, J., Lyu, M.: Loghub: A large collection of system log datasets for ai-driven log analytics. In: 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), pp. 355\u2013366. IEEE (2023)","DOI":"10.1109\/ISSRE59848.2023.00071"},{"key":"9912_CR46","doi-asserted-by":"crossref","unstructured":"Xu, W., Huang, L., Fox, A., Patterson, D., Jordan, M.I.: Detecting large-scale system problems by mining console logs. In: Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles, pp. 117\u2013132 (2009)","DOI":"10.1145\/1629575.1629587"},{"key":"9912_CR47","doi-asserted-by":"crossref","unstructured":"Vaarandi, R., Pihelgas, M.: Logcluster-a data clustering and pattern mining algorithm for event logs. In: 2015 11th International Conference on Network and Service Management (CNSM), pp. 1\u20137. IEEE (2015)","DOI":"10.1109\/CNSM.2015.7367331"},{"key":"9912_CR48","unstructured":"Lou, J.G., Fu, Q., Yang, S., et al.: Mining invariants from console logs for system problem detection. In: Proceedings of the 2010 USENIX Annual Technical Conference (USENIX ATC 10) (2010)"},{"key":"9912_CR49","doi-asserted-by":"crossref","unstructured":"Yin, K., Yan, M., Xu, L., et al.: Improving log-based anomaly detection with component-aware analysis. In: Proceedings of the 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 667\u2013671. IEEE (2020)","DOI":"10.1109\/ICSME46990.2020.00069"},{"key":"9912_CR50","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, Z., Ni, J., et al.: Multi-scale one-class recurrent neural networks for discrete event sequence anomaly detection. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3726\u20133734 (2021)","DOI":"10.1145\/3447548.3467125"},{"key":"9912_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109860","volume":"132","author":"C Zhang","year":"2023","unstructured":"Zhang, C., Wang, X., Zhang, H., et al.: LayerLog: log sequence anomaly detection based on hierarchical semantics. Appl. Soft Comput. 132, 109860 (2023)","journal-title":"Appl. Soft Comput."},{"key":"9912_CR52","doi-asserted-by":"publisher","unstructured":"Chen, X., Zhang, S., Jing, Y., Wang, S.: TRGATLog: log anomaly detection method based on log time relation graph attention network. Appl. Res. Comput. 41, 1034\u20131040 (2024) (in Chinese). https:\/\/doi.org\/10.19734\/j.issn.1001-3695.2023.07.0365","DOI":"10.19734\/j.issn.1001-3695.2023.07.0365"},{"key":"9912_CR53","unstructured":"Chen, L., Song, C., Wang, X., Fu, D., Li, F.: CSCLog: a component subsequence correlation-aware log anomaly detection method. arXiv preprint arXiv:2307.03359 (2023)"},{"key":"9912_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103808","volume":"140","author":"Z Yin","year":"2024","unstructured":"Yin, Z., Kong, X., Yin, C.: Semi-supervised log anomaly detection based on bidirectional temporal convolution network. Comput. Secur. 140, 103808 (2024)","journal-title":"Comput. Secur."}],"container-title":["Journal of Network and Systems Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10922-025-09912-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10922-025-09912-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10922-025-09912-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T11:49:38Z","timestamp":1744976978000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10922-025-09912-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,20]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["9912"],"URL":"https:\/\/doi.org\/10.1007\/s10922-025-09912-5","relation":{},"ISSN":["1064-7570","1573-7705"],"issn-type":[{"value":"1064-7570","type":"print"},{"value":"1573-7705","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,20]]},"assertion":[{"value":"27 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"33"}}