[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126313-en":3,"doc-seo-126313-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126313,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Resilient Anomaly Detection in Fiber-Optic Networks - A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring","A comprehensive machine-learning framework is presented for robust anomaly identification in optical fiber networks using real-time state-of-polarization (SOP) monitoring. SOP data are exploited under three threat scenarios: malicious or critical vibration events, overlapping mechanical disturbances, and malicious fiber tapping for eavesdropping. Supervised models including k-nearest neighbor, random forest, XGBoost, and decision trees classify vibration events, while resilience is evaluated under background interference by superimposed sinusoidal noise at varied frequencies, clarifying detection fidelity under ambient vibrations and informing noise-mitigation needs.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nResilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring  \nOriginal  \nResilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring / Malik, Gulmina; Dipto, Imran Chowdhury; Masood, Muhammad Umar;  \nMohamed, Mashboob Cheruvakkadu; Straullu, Stefano; Bhyri, Sai Kishore; Galimberti, Gabriele Maria; Napoli, Antonio; Pedro, João; Wakim, Walid; Curri, Vittorio. -In: AI. -ISSN 2673-2688. -6:7(2025) . [10 .3390/ai6070131]  \nAvailability:  \nThis version is available at: 11583/3001170 since: 2025-06-20T13:45:06Z  \nPublisher: MDPI  \nPublished  \nDOI:10.3390/ai6070131  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n04 October 2025  \nAI  \nArticle  \nResilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring  \nGulmina Malik 1, *, Imran Chowdhury Dipto 1, Muhammad Umar Masood 1,  \nMashboob Cheruvakkadu Mohamed 1, Stefano Straullu 2, Sai Kishore Bhyri 3, Gabriele Maria Galimberti 4, Antonio Napoli 5, João Pedro 6, Walid Wakim 7 and Vittorio Curri 1  \nAcademic Editor: Yibeltal Chanie Manie  \nReceived: 16 May 2025  \nRevised: 9 June 2025  \nAccepted: 13 June 2025  \nPublished: 20 June 2025  \nCitation: Malik , G.; Dipto, I.C.; Masood, M.U.; Mohamed, M.C.; Straullu, S.; Bhyri, S.K.; Galimberti, G.M.; Napoli, A.; Pedro, J.; Wakim, W.;  \net al. Resilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-ofPolarization Monitoring. AI 2025, 6, 131. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ai6070131  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Electronics and Telecommunications, Polytechnic University of Turin, 10129 Turin, Italy;  \nimran.dipto@polito.it (I.C.D.); muhammad.masood@polito.it (M.U.M.);  \nmashboob.cheruvakkadu@polito.it (M.C.M.); vittorio.curri@polito.it (V.C.)  \n2 LINKS Foundation, 10129 Turin, Italy; [stefano.straullu@linksfoundation.com](stefano.straullu@linksfoundation.com)  \n3 Optical Networks, Nokia, Bangalore 560045, India; [sai.bhyri@nokia.com](sai.bhyri@nokia.com)  \n4 Optical Networks, Nokia, 20060 Milan, Italy; [gabriele.galimberti@nokia.com](gabriele.galimberti@nokia.com)  \n5 Optical Networks, Nokia, 81541 Munich, Germany; [antonio.napoli@nokia.com](antonio.napoli@nokia.com)  \n6 Optical Networks, Nokia, 2720-092 Carnaxide, Portugal; [joao.pedro@nokia.com](joao.pedro@nokia.com)  \n7 Optical Networks, Nokia, Naperville, IL 60563, USA; [walid.wakim@nokia.com](walid.wakim@nokia.com)  \n* Correspondence: gulmina.malik@polito.it  \nAbstract  \nWe present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events,(ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping) . We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effect","cbCaiu9iilzZF63A","https://ap.wps.com/l/cbCaiu9iilzZF63A","pdf",3150849,1,22,"English","en",105,"# Abstract\n# Introduction\n## Optical networks and reliability needs\n## Environmental monitoring with pre-installed fiber infrastructures\n## Fiber sensitivity and sensing overview\n# Related methods and framework design","[{\"question\":\"What monitoring signal does the framework use for anomaly detection in fiber networks?\",\"answer\":\"It uses real-time state-of-polarization (SOP) monitoring data to derive polarization signatures for detecting anomalies.\"},{\"question\":\"Which threats and interference types are considered in the study?\",\"answer\":\"Three threat scenarios are used: malicious/critical vibrations, overlapping mechanical disturbances, and malicious fiber tapping. Resilience is tested by superimposing sinusoidal noise at different frequencies to model background interference.\"},{\"question\":\"How does the system identify and classify vibration events?\",\"answer\":\"It applies supervised machine learning classifiers such as k-NN, random forest, XGBoost, and decision trees to classify the vibration events based on SOP-derived features.\"}]","Resilient Anomaly Detection in Fiber-Optic Networks - A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring | PDF",1785904401,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"resilient-anomaly-detection-in-fiber-optic-networks-a-machine-learning-framework-for-multi-threat-identification-using-state-of-polarization-monitoring","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/resilient-anomaly-detection-in-fiber-optic-networks-a-machine-learning-framework-for-multi-threat-identification-using-state-of-polarization-monitoring/126313/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What monitoring signal does the framework use for anomaly detection in fiber networks?","Question",{"text":76,"@type":77},"It uses real-time state-of-polarization (SOP) monitoring data to derive polarization signatures for detecting anomalies.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which threats and interference types are considered in the study?",{"text":81,"@type":77},"Three threat scenarios are used: malicious/critical vibrations, overlapping mechanical disturbances, and malicious fiber tapping. Resilience is tested by superimposing sinusoidal noise at different frequencies to model background interference.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the system identify and classify vibration events?",{"text":85,"@type":77},"It applies supervised machine learning classifiers such as k-NN, random forest, XGBoost, and decision trees to classify the vibration events based on SOP-derived features.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]