{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T14:03:24Z","timestamp":1760623404828,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031744976"},{"type":"electronic","value":"9783031744983"}],"license":[{"start":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T00:00:00Z","timestamp":1729382400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T00:00:00Z","timestamp":1729382400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-74498-3_23","type":"book-chapter","created":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T11:02:30Z","timestamp":1729335750000},"page":"322-337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Anomaly Detection Within Mission-Critical Call Processing"],"prefix":"10.1007","author":[{"given":"Sean","family":"Doris","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2810-2781","authenticated-orcid":false,"given":"Iosif","family":"Salem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7798-1711","authenticated-orcid":false,"given":"Stefan","family":"Schmid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,20]]},"reference":[{"key":"23_CR1","unstructured":"Rust Package Registry. https:\/\/crates.io\/ (2022)"},{"key":"23_CR2","unstructured":"Aurelien, G.: Hands-On Machine Learning with Scikit\u2013Learn and TensorFlow. O\u2019Reilly Media (2017)"},{"key":"23_CR3","doi-asserted-by":"publisher","unstructured":"Avdotin, E., Bankov, D., Khorov, E., Lyakhov, A.: OFDMA resource allocation for real-time applications in IEEE 802.11ax networks. In: 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), pp.\u00a01\u20133 (2019). https:\/\/doi.org\/10.1109\/BlackSeaCom.2019.8812774","DOI":"10.1109\/BlackSeaCom.2019.8812774"},{"issue":"4","key":"23_CR4","doi-asserted-by":"publisher","first-page":"3421","DOI":"10.1007\/s10586-020-03096-0","volume":"23","author":"S Azizi","year":"2020","unstructured":"Azizi, S., Zandsalimi, M., Li, D.: An energy-efficient algorithm for virtual machine placement optimization in cloud data centers. Clust. Comput. 23(4), 3421\u20133434 (2020). https:\/\/doi.org\/10.1007\/s10586-020-03096-0","journal-title":"Clust. Comput."},{"key":"23_CR5","doi-asserted-by":"publisher","unstructured":"Barber, D.: Bayesian Reasoning and Machine Learning, 1 edn. Cambridge University Press (2012). https:\/\/doi.org\/10.1017\/CBO9780511804779","DOI":"10.1017\/CBO9780511804779"},{"key":"23_CR6","unstructured":"Casas, P., Fiadino, P., D\u2019Alconzo, A.: Machine-Learning Based Approaches for Anomaly Detection and Classification in Cellular Networks. TMA (Apr 2016)"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1\u201327:27 (2011)","DOI":"10.1145\/1961189.1961199"},{"key":"23_CR8","doi-asserted-by":"publisher","unstructured":"Ciocarlie, G.F., Lindqvist, U., Nov\u00e1czki, S., Sanneck, H.: Detecting anomalies in cellular networks using an ensemble method. In: Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013), pp. 171\u2013174 (2013). https:\/\/doi.org\/10.1109\/CNSM.2013.6727831","DOI":"10.1109\/CNSM.2013.6727831"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Doris, S., Salem, I., Schmid, S.: Anomaly detection within mission-critical call processing (2024). http:\/\/arxiv.org\/abs\/2408.14599","DOI":"10.1007\/978-3-031-74498-3_23"},{"key":"23_CR10","unstructured":"Gayraud, R., Jacques, O., Robert, D., Charles, W.: SIPp (2014). http:\/\/sipp.sourceforge.net\/doc\/reference.html"},{"key":"23_CR11","unstructured":"Godard, S.: iostat(1)\u2014Linux man"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Gulenko, A., Schmidt, F., Acker, A., Wallschl\u00e4ger, M., Kao, O., Liu, F.: Detecting anomalous behavior of black-box services modeled with distance-based online clustering. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 912\u2013915 (2018). https:\/\/doi.org\/10.1109\/CLOUD.2018.00134","DOI":"10.1109\/CLOUD.2018.00134"},{"key":"23_CR13","doi-asserted-by":"publisher","unstructured":"Gulenko, A., Wallschl\u00e4ger, M., Schmidt, F., Kao, O., Liu, F.: Evaluating machine learning algorithms for anomaly detection in clouds. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 2716\u20132721 (2016). https:\/\/doi.org\/10.1109\/BigData.2016.7840917","DOI":"10.1109\/BigData.2016.7840917"},{"key":"23_CR14","doi-asserted-by":"publisher","unstructured":"Jin, S., Zhang, Z., Chakrabarty, K., Gu, X.: Accurate anomaly detection using correlation-based time-series analysis in a core router system. In: 2016 IEEE International Test Conference (ITC), pp. 1\u201310 (2016). https:\/\/doi.org\/10.1109\/TEST.2016.7805836","DOI":"10.1109\/TEST.2016.7805836"},{"issue":"11","key":"23_CR15","doi-asserted-by":"publisher","first-page":"3880","DOI":"10.1109\/TCSI.2020.3010743","volume":"67","author":"K Khalil","year":"2020","unstructured":"Khalil, K., Eldash, O., Kumar, A., Bayoumi, M.: Machine learning-based approach for hardware faults prediction. IEEE Trans. Circ. Syst. I 67(11), 3880\u20133892 (2020). https:\/\/doi.org\/10.1109\/TCSI.2020.3010743","journal-title":"IEEE Trans. Circ. Syst. I"},{"key":"23_CR16","unstructured":"King, C.I.: stress-ng (stress next generation) (2022). https:\/\/github.com\/ColinIanKing\/stress-ng"},{"key":"23_CR17","doi-asserted-by":"publisher","unstructured":"Liu, Y., Zhang, J., Jiang, M., Raymer, D., Strassner, J.: A case study: a model-based approach to retrofit a network fault management system with self-healing functionality. In: 15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS 2008), pp. 9\u201318 (2008). https:\/\/doi.org\/10.1109\/ECBS.2008.30","DOI":"10.1109\/ECBS.2008.30"},{"key":"23_CR18","unstructured":"Mostafa, Y.A., Magdon-Ismail, M., Lin, H.T.: Learning From Data, 1st edn. AMLBook (2017)"},{"key":"23_CR19","unstructured":"Program, C.N.A. (ed.): Connecting Networks. Companion Guide, 1st edn. Cisco Press, Indianapolis, Indiana (2014)"},{"key":"23_CR20","first-page":"53","volume":"1","author":"O Romanov","year":"2020","unstructured":"Romanov, O., Nesterenko, M., Fesokha, N., Mankivskyi, V.: Evaluation of productivity virtualization technologies of switching equipment telecommunications networks. Inf. Telecommun. Sci. 1, 53\u201358 (2020)","journal-title":"Inf. Telecommun. Sci."},{"key":"23_CR21","doi-asserted-by":"publisher","unstructured":"Samir, A., Pahl, C.: Detecting and predicting anomalies for edge cluster environments using hidden Markov models. In: 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), pp. 21\u201328 (2019). https:\/\/doi.org\/10.1109\/FMEC.2019.8795337","DOI":"10.1109\/FMEC.2019.8795337"},{"key":"23_CR22","doi-asserted-by":"publisher","unstructured":"Sauvanaud, C., Lazri, K., Ka\u00e2niche, M., Kanoun, K.: Towards black-box anomaly detection in virtual network functions. In: 2016 46th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W), pp. 254\u2013257 (2016). https:\/\/doi.org\/10.1109\/DSN-W.2016.17","DOI":"10.1109\/DSN-W.2016.17"},{"issue":"5","key":"23_CR23","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1162\/089976600300015565","volume":"12","author":"B Sch\u00f6lkopf","year":"2000","unstructured":"Sch\u00f6lkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207\u20131245 (2000). https:\/\/doi.org\/10.1162\/089976600300015565","journal-title":"Neural Comput."},{"key":"23_CR24","unstructured":"Segaran, T.: Programming Collective Intelligence. O\u2019Reily Media (2007)"},{"key":"23_CR25","doi-asserted-by":"publisher","unstructured":"Tao, C., Li, T., Huang, J.: Kernel choice in one-class support vector machines for novelty and outlier detection. In: 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp. 116\u2013120 (2020). https:\/\/doi.org\/10.1109\/MLBDBI51377.2020.00026","DOI":"10.1109\/MLBDBI51377.2020.00026"},{"issue":"4","key":"23_CR26","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1109\/TPDS.2018.2870403","volume":"30","author":"O Tuncer","year":"2019","unstructured":"Tuncer, O., Ates, E., Zhang, Y., Turk, A., Brandt, J., Leung, V.J., Egele, M., Coskun, A.K.: Online diagnosis of performance variation in HPC systems using machine learning. IEEE Trans. Parallel Distrib. Syst. 30(4), 883\u2013896 (2019). https:\/\/doi.org\/10.1109\/TPDS.2018.2870403","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"23_CR27","unstructured":"Ware, H., Frederick, F.: VMSTAT(8): Report virtual memory statistics-Linux Man"},{"key":"23_CR28","unstructured":"Welsh, M., Cox, A., Hoang, T., Eckenfels, B.: NETSTAT(8)-Linux man"},{"issue":"7697","key":"23_CR29","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1038\/d41586-018-02881-7","volume":"555","author":"D Wong","year":"2018","unstructured":"Wong, D., Yip, S.: Machine learning classifies cancer. Nature 555(7697), 446\u2013447 (2018). https:\/\/doi.org\/10.1038\/d41586-018-02881-7","journal-title":"Nature"},{"key":"23_CR30","unstructured":"Zhang, H.: The Optimality of Naive Bayes. 2 (2004)"}],"container-title":["Lecture Notes in Computer Science","Stabilization, Safety, and Security of Distributed Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-74498-3_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T22:03:49Z","timestamp":1735596229000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74498-3_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,20]]},"ISBN":["9783031744976","9783031744983"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74498-3_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,20]]},"assertion":[{"value":"20 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Stabilizing, Safety, and Security of Distributed Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nagoya","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sss2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sss2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}