{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T22:32:45Z","timestamp":1761172365816,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032032805","type":"print"},{"value":"9783032032812","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"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\/978-3-032-03281-2_15","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T04:57:34Z","timestamp":1761109054000},"page":"220-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Anomaly Detection in\u00a0Cellular Modem Metrics Using Deep Autoencoders"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6955-830X","authenticated-orcid":false,"given":"Nikita","family":"Smirnov","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9478-8305","authenticated-orcid":false,"given":"Mika","family":"Friesenborg","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-8915","authenticated-orcid":false,"given":"Sven","family":"Tomforde","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"15_CR1","unstructured":"3GPP: 3rd Generation Partnership Project; Technical Specification Group Radio Access Network. Technical report, 3GPP (2017). https:\/\/portal.3gpp.org\/#\/. Accessed 25 July 2025"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Azmin, T., Ahmadinejad, M., Shahriar, N.: Bandwidth prediction in 5G mobile networks using informer. In: 2022 13th International Conference on Network of the Future (NoF), pp.\u00a01\u20139. IEEE (2022)","DOI":"10.1109\/NoF55974.2022.9942521"},{"issue":"105","key":"15_CR3","first-page":"1","volume":"25","author":"R Bouman","year":"2024","unstructured":"Bouman, R., Bukhsh, Z., Heskes, T.: Unsupervised anomaly detection algorithms on real-world data: how many do we need? J. Mach. Learn. Res. 25(105), 1\u201334 (2024)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR4","unstructured":"CAPTN Fjord5G (F\u00f6rde5G): https:\/\/captn.sh\/en\/foerde-5g-englisch\/. Accessed 25 July 2025"},{"issue":"12","key":"15_CR5","doi-asserted-by":"publisher","first-page":"7103","DOI":"10.1002\/int.22582","volume":"36","author":"Z Cheng","year":"2021","unstructured":"Cheng, Z., Wang, S., Zhang, P., Wang, S., Liu, X., Zhu, E.: Improved autoencoder for unsupervised anomaly detection. Int. J. Intell. Syst. 36(12), 7103\u20137125 (2021)","journal-title":"Int. J. Intell. Syst."},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Denizer, B., Landsiedel, O.: Fjord5G: A comprehensive 5G dataset for coastal maritime connectivity. In: 2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring), pp.\u00a01\u20135 (2025)","DOI":"10.1109\/VTC2025-Spring65109.2025.11174898"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Han, J., Liu, T., Ma, J., Zhou, Y., Zeng, X., Xu, Y.: Anomaly detection and early warning model for latency in private 5G networks. Appl. Sci. 12(23) (2022)","DOI":"10.3390\/app122312472"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Han, S., Hu, X., Huang, H., Jiang, M., Zhao, Y.: ADBench: anomaly detection benchmark (2022). https:\/\/arxiv.org\/abs\/2206.09426","DOI":"10.2139\/ssrn.4266498"},{"issue":"10","key":"15_CR9","doi-asserted-by":"publisher","first-page":"13327","DOI":"10.1109\/TNNLS.2023.3267028","volume":"35","author":"M Kim","year":"2024","unstructured":"Kim, M., Yu, J., Kim, J., Oh, T.H., Choi, J.K.: An iterative method for unsupervised robust anomaly detection under data contamination. IEEE Trans. Neural Netw. Learn. Syst. 35(10), 13327\u201313339 (2024)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"15_CR10","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes (2013). https:\/\/arxiv.org\/abs\/1312.6114"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Li, P., Pei, Y., Li, J.: A comprehensive survey on design and application of autoencoder in deep learning. Appl. Soft Comput. 138 (2023)","DOI":"10.1016\/j.asoc.2023.110176"},{"key":"15_CR12","doi-asserted-by":"publisher","first-page":"86810","DOI":"10.1109\/ACCESS.2019.2924673","volume":"7","author":"Y Liao","year":"2019","unstructured":"Liao, Y., Yao, H., Hua, Y., Li, C.: CSI feedback based on deep learning for massive MIMO systems. IEEE Access 7, 86810\u201386820 (2019)","journal-title":"IEEE Access"},{"key":"15_CR13","unstructured":"Makhzani, A., Frey, B.: k-sparse autoencoders (2014). https:\/\/arxiv.org\/abs\/1312.5663"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"M\u00fcller-Schloer, C., Tomforde, S.: Organic Computing - technical systems for survival in the real world. Birkh\u00e4user (2017)","DOI":"10.1007\/978-3-319-68477-2"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Munir, M., Chattha, M.A., Dengel, A., Ahmed, S.: A comparative analysis of traditional and deep learning-based anomaly detection methods for streaming data. In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pp. 561\u2013566 (2019)","DOI":"10.1109\/ICMLA.2019.00105"},{"key":"15_CR16","unstructured":"Noonari, N., Corujo, D., Aguiar, R.L., Ferrao, F.J.: Multi-scale convolutional LSTM with transfer learning for anomaly detection in cellular networks (2024). https:\/\/arxiv.org\/abs\/2410.03732"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"O\u011fuz, H.T., Kalayc\u0131o\u011flu, A.: Anomaly detection in multi-tiered cellular networks using LSTM and 1D CNN. EURASIP J. Wirel. Commun. Netw. 2022(1) (2022)","DOI":"10.1186\/s13638-022-02183-7"},{"key":"15_CR18","unstructured":"Paszke, A., et\u00a0al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035. Curran Associates, Inc. (2019)"},{"key":"15_CR19","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: explicit invariance during feature extraction. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011 (2011)","DOI":"10.1007\/978-3-642-23783-6_41"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Sangaiah, A.K., Rezaei, S., Javadpour, A., Miri, F., Zhang, W., Wang, D.: Automatic fault detection and diagnosis in cellular networks and beyond 5G: Intell. Netw. Manage. Algorithms 15(11) (2022)","DOI":"10.3390\/a15110432"},{"key":"15_CR22","doi-asserted-by":"publisher","first-page":"59406","DOI":"10.1109\/ACCESS.2021.3072916","volume":"9","author":"M Savic","year":"2021","unstructured":"Savic, M.: Deep learning anomaly detection for cellular IoT with applications in smart logistics. IEEE Access 9, 59406\u201359419 (2021)","journal-title":"IEEE Access"},{"key":"15_CR23","unstructured":"Singh, A., Weber, M., Lange-Hegermann, M.: Interpretable anomaly detection in cellular networks by learning concepts in variational autoencoders (2023). https:\/\/arxiv.org\/abs\/2306.15938"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Smirnov, N., Tomforde, S.: Real-time data transmission optimization on 5G remote-controlled units using deep reinforcement learning. In: Architecture of Computing Systems: 36th International Conference, Athens, Greece, June 13\u201315, 2023, pp. 274\u2013289. Springer, Cham (2023)","DOI":"10.1007\/978-3-031-42785-5_19"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 1096\u20131103 (2008)","DOI":"10.1145\/1390156.1390294"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Xu, Z., Saleh, J.H.: Machine learning for reliability engineering and safety applications: review of current status and future opportunities. Reliab. Eng. Syst. Saf. 211 (2021)","DOI":"10.1016\/j.ress.2021.107530"},{"issue":"1","key":"15_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3691338","volume":"57","author":"Z Zamanzadeh Darban","year":"2024","unstructured":"Zamanzadeh Darban, Z., Webb, G.I., Pan, S., Aggarwal, C., Salehi, M.: Deep learning for time series anomaly detection: a survey. ACM Comput. Surv. 57(1), 1\u201342 (2024)","journal-title":"ACM Comput. Surv."}],"container-title":["Lecture Notes in Computer Science","Architecture of Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-03281-2_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T04:57:44Z","timestamp":1761109064000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-03281-2_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,23]]},"ISBN":["9783032032805","9783032032812"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-03281-2_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,23]]},"assertion":[{"value":"23 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Architecture of Computing Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kiel","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"22 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"38","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"arcs2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/arcs-conference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}