[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122405-en":3,"doc-seo-122405-105":29,"detail-sidebar-cat-0-en-105":89},{"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":4,"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":11},122405,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Intelligent Detection of Overlapping Fiber Anomalies in Optical Networks Using Machine Learning","A machine learning framework is presented to detect overlapping fiber anomalies in optical communication networks by leveraging state-of-polarization dynamics. The method models concurrent mechanical disturbances and uses simulated disturbances combined with XGBoost classification to distinguish overlapping events. Results show near-perfect classification accuracy under noise conditions, supporting reliable identification of multiple simultaneous anomaly signatures. The approach strengthens fault detection and improves physical-layer security by enabling earlier, more precise responses to interacting fiber faults and hostile intrusions.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nIntelligent Detection of Overlapping Fiber Anomalies in Optical Networks Using Machine Learning  \nOriginal  \nIntelligent Detection of Overlapping Fiber Anomalies in Optical Networks Using Machine Learning / Malik, Gulmina; Dipto, Imran Chowdhury; Masood, Muhammad Umar; Cheruvakkadu Mohamed, Mashboob; Straullu, Stefano; Kishore Bhyri, Sai; Maria Galimberti, Gabriele; Pedro, João; Napoli, Antonio; Wakim, Walid; Curri, Vittorio. - (2025) . (Intervento presentato al convegno 2025 IEEE Photonics Society Summer Topicals tenutosi a Berlin (Ger) nel 21-23 Luglio 2025) .  \nAvailability:  \nThis version is available at: 11583/3002698 since: 2025-09-01T14:29:46Z  \nPublisher: IEEE  \nPublished DOI:  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n04 October 2025  \nIntelligent Detection of Overlapping Fiber Anomalies in Optical Networks Using Machine  \nLearning  \nGulmina Malik Imran Chowdhury Dipto Muhammad Umar Masood Mashboob Cheruvakkadu Mohamed  \nPolitecnico di Torino, Italy Politecnico di Torino, Italy Politecnico di Torino, Italy Politecnico di Torino, Italy  \ngulmina.malik@polito.it imran.dipto@polito.it muhammad.masood@polito.it mashboob.cheruvakkadu@polito.it  \nStefano Straullu  \nLinks Foundation, Italy [stefano.straullu@linksfoundation.com](stefano.straullu@linksfoundation.com)  \nAntonio Napoli  \nNokia [antonio.napoli@nokia.com](antonio.napoli@nokia.com)  \nSai Kishore Bhyri  \nNokia  \n[sai.bhyri@nokia.com](sai.bhyri@nokia.com)  \nGabriele Maria Galimberti  \nNokia  \n[gabriele.galimberti@nokia.com](gabriele.galimberti@nokia.com)  \nJoo Pedro  \nNokia  \n[joao.pedro@nokia.com](joao.pedro@nokia.com)  \nWalid Wakim  \nNokia  \n[walid.wakim@nokia.com](walid.wakim@nokia.com)  \nVittorio Curri  \nPolitecnico di Torino, Italy vittorio.curri@polito.it  \nAbstract—We propose a machine learning approach leveraging state-of-polarization dynamics to detect overlapping fiber anomalies. Simulated disturbances and XGBoost classification achieve near-perfect accuracy under noise, enabling precise identification of concurrent events and enhancing both fault detection and physical layer security in optical communication networks.  \nIndex Terms—Machine learning, XGBoost, state of polarization, optical fiber, fiber anomalies.  \nI. INTRODUCTION  \nFiber optic networks form the backbone of global telecommunications, enabling high-speed data transmission with minimal latency. However, as these networks evolve, they become increasingly susceptible to overlapping anomalies such as bends, breaks, or splice losses. These overlapping disturbances complicate identification, necessitating more sophisticated detection techniques [1] . Traditional techniques, such as Optical Time-Domain Reflectometry (OTDR) and thresholdbased monitoring, rely on predefined heuristics and struggle to differentiate the overlapping anomalies, particularly when multiple fault signatures interact [1, 2] . Recent advancementsin machine learning (ML) have enabled more sophisticated anomaly detection in optical networks. For instance, in [3], a vision transformer-based model was introduced to identify and locate simultaneous anomaly occurrences; however, it requires a large amount of processing power for training and inference. These challenges highlight the need for intelligent real-time anomaly detection systems that can take preventive measures without compromising the network integrity and econo","cbCaiijLBNDBV7qY","https://ap.wps.com/l/cbCaiijLBNDBV7qY","pdf",395128,1,3,"English","en",105,"# Introduction\n# Polarization-Based Anomaly Detection Setup","[{\"question\":\"What overlapping anomalies does the study aim to detect in optical fiber networks?\",\"answer\":\"The study targets overlapping fiber disturbances such as mechanical disturbances that produce interacting fault signatures, including events that can involve bends, breaks, or splice-loss related disturbances.\"},{\"question\":\"How does the proposed approach use state-of-polarization information?\",\"answer\":\"It monitors polarization changes via Stokes-parameter dynamics and SOP-based signatures, including SOPAS-based characterization, to represent each disrupting event distinctly for ML classification.\"},{\"question\":\"Which machine learning model is used, and what performance is reported?\",\"answer\":\"The approach uses XGBoost for classification and reports near-perfect accuracy under noise, enabling precise identification of concurrent overlapping events.\"}]","Intelligent Detection of Overlapping Fiber Anomalies in Optical Networks Using Machine Learning | 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