{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:03:03Z","timestamp":1763190183822,"version":"3.45.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032104656","type":"print"},{"value":"9783032104663","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-10466-3_26","type":"book-chapter","created":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T06:59:02Z","timestamp":1763189942000},"page":"315-326","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Unsupervised Learning Log Anomaly Detection Method Based on\u00a0Graph Neural Network"],"prefix":"10.1007","author":[{"given":"Xianlang","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangsheng","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2383-1370","authenticated-orcid":false,"given":"Xinling","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangying","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwu","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"issue":"2","key":"26_CR1","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1145\/3575637.3575652","volume":"24","author":"Z Li","year":"2022","unstructured":"Li, Z., van Leeuwen, M.: Feature selection for fault detection and prediction based on event log analysis. ACM SIGKDD Explorations Newsl. 24(2), 96\u2013104 (2022)","journal-title":"ACM SIGKDD Explorations Newsl."},{"key":"26_CR2","unstructured":"Kimanzi, R., Kimanga, P., Cherori, D., Gikunda, P.K.: Deep learning algorithms used in intrusion detection systems\u2013a review. arXiv preprint arXiv:2402.17020, 2024"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, W., et al.: Lograg: semi-supervised log-based anomaly detection with retrieval-augmented generation. In: 2024 IEEE International Conference on Web Services (ICWS), pp. 1100\u20131102. IEEE, 2024","DOI":"10.1109\/ICWS62655.2024.00129"},{"issue":"12","key":"26_CR4","doi-asserted-by":"publisher","first-page":"12095","DOI":"10.1109\/TKDE.2021.3139086","volume":"35","author":"W Gan","year":"2021","unstructured":"Gan, W., Chen, L., Wan, S., Chen, J., Chen, C.-M.: Anomaly rule detection in sequence data. IEEE Trans. Knowl. Data Eng. 35(12), 12095\u201312108 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"26_CR5","unstructured":"Liu, J., et al.: Log-based anomaly detection based on evt theory with feedback. arXiv preprint arXiv:2306.05032, 2023"},{"key":"26_CR6","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":"26_CR7","doi-asserted-by":"crossref","unstructured":"He, S., Lin, Q., Lou, J.G., Zhang, H., Lyu, M.R., Zhang, D.: Identifying impactful service system problems via log analysis. In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 60\u201370, 2018","DOI":"10.1145\/3236024.3236083"},{"issue":"2","key":"26_CR8","doi-asserted-by":"publisher","first-page":"2863","DOI":"10.1109\/TCSS.2023.3297233","volume":"11","author":"R Luo","year":"2023","unstructured":"Luo, R., Krishnamurthy, V.: Fr\u00e9chet-statistics-based change point detection in dynamic social networks. IEEE Trans. Comput. Soc. Syst. 11(2), 2863\u20132871 (2023)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"26_CR9","volume":"12","author":"M Landauer","year":"2023","unstructured":"Landauer, M., Onder, S., Skopik, F., Wurzenberger, M.: Deep learning for anomaly detection in log data: a survey. Mach. Learn. Appl. 12, 100470 (2023)","journal-title":"Mach. Learn. Appl."},{"key":"26_CR10","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":"26_CR11","doi-asserted-by":"crossref","unstructured":"Meng, W., et al.: Loganomaly: unsupervised detection of sequential and quantitative anomalies in unstructured logs. In: IJCAI, vol. 19, pp. 4739\u20134745 (2019)","DOI":"10.24963\/ijcai.2019\/658"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Liu, P., et\u00a0al.: Unsupervised detection of microservice trace anomalies through service-level deep bayesian networks. In: 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), pp. 48\u201358. IEEE, 2020","DOI":"10.1109\/ISSRE5003.2020.00014"},{"key":"26_CR13","unstructured":"Zhao, L., Sawlani, S., Srinivasan, A., Akoglu, L.: Graph anomaly detection with unsupervised gnns. arXiv preprint arXiv:2210.09535, 2022"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Ma, R., Pang, G., Chen, L., Van Den Hengel, A.: Deep graph-level anomaly detection by glocal knowledge distillation. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 704\u2013714, 2022","DOI":"10.1145\/3488560.3498473"},{"issue":"4","key":"26_CR15","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1109\/TCYB.2018.2804940","volume":"49","author":"X Miao","year":"2018","unstructured":"Miao, X., Liu, Y., Zhao, H., Li, C.: Distributed online one-class support vector machine for anomaly detection over networks. IEEE Trans. Cybern. 49(4), 1475\u20131488 (2018)","journal-title":"IEEE Trans. Cybern."},{"issue":"4","key":"26_CR16","doi-asserted-by":"publisher","first-page":"4119","DOI":"10.1109\/TNSM.2021.3125967","volume":"18","author":"C Zhang","year":"2021","unstructured":"Zhang, C., Wang, X., Zhang, H., Zhang, H., Han, P.: Log sequence anomaly detection based on local information extraction and globally sparse transformer model. IEEE Trans. Netw. Serv. Manag. 18(4), 4119\u20134133 (2021)","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"issue":"6","key":"26_CR17","doi-asserted-by":"publisher","first-page":"2517","DOI":"10.1007\/s10618-023-00967-z","volume":"37","author":"Z Li","year":"2023","unstructured":"Li, Z., Van Leeuwen, M.: Explainable contextual anomaly detection using quantile regression forests. Data Min. Knowl. Disc. 37(6), 2517\u20132563 (2023)","journal-title":"Data Min. Knowl. Disc."},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461, 2019","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"He, P., Zhu, J., Zheng, Z., Lyu, M.R.: Drain: an online log parsing approach with fixed depth tree. In: 2017 IEEE International Conference on Web Services (ICWS), pp. 33\u201340. IEEE, 2017","DOI":"10.1109\/ICWS.2017.13"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-bert: sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084, 2019","DOI":"10.18653\/v1\/D19-1410"},{"key":"26_CR21","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR, 2017"},{"key":"26_CR22","unstructured":"Tong, Z., Liang, Y., Sun, C., Li, X., Rosenblum, D., Lim, A.: Digraph inception convolutional networks. Adv. Neural Inf. Process. Syst. 33, 17907\u201317918 (2020)"},{"key":"26_CR23","unstructured":"Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393\u20134402. PMLR, 2018"},{"key":"26_CR24","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":"26_CR25","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":"26_CR26","doi-asserted-by":"crossref","unstructured":"Zhu, J., et al.: Tools and benchmarks for automated log parsing. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 121\u2013130. IEEE, 2019","DOI":"10.1109\/ICSE-SEIP.2019.00021"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Yang, L., et al.: Semi-supervised log-based anomaly detection via probabilistic label estimation. In: 2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE), pp. 1448\u20131460. IEEE, 2021","DOI":"10.1109\/ICSE43902.2021.00130"}],"container-title":["Lecture Notes in Computer Science","Network and Parallel Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-10466-3_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T06:59:08Z","timestamp":1763189948000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-10466-3_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,16]]},"ISBN":["9783032104656","9783032104663"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-10466-3_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,16]]},"assertion":[{"value":"16 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"NPC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Network and Parallel Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nha Trang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"npc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.npc-conference.com\/#\/npc2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}