Optimizing Optical Fiber Faults Detection:A Comparative Analysis of Advanced Machine Learning Approaches

作     者：Kamlesh Kumar Soothar Yuanxiang Chen Arif Hussain Magsi Cong Hu Hussain Shah 

作者机构：School of Electronics EngineeringBeijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Integrated Services NetworksXidian UniversityXi’an710071China State Key Laboratory ofNetworking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第79卷第5期

页      面：2697-2721页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：in part by the National Natural Science Foundation of China under Grants 62271079,61875239,62127802 in part by the Fundamental Research Funds for the Central Universities under Grant 2023PY01 in part by the National Key Research and Development Program of China under Grant 2018YFB2200903 in part by the Beijing Nova Program with Grant Number Z211100002121138 

主　　题：Fiber optics fault detection multiclassification machine learning ensemble learning 

摘      要：Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)*** emerging faultsin optical networks introduce challenges that can jeopardize the network with a variety of *** existingliterature witnessed various partial or inadequate *** the other hand,Machine Learning(ML)hasrevolutionized as a promising technique for fault detection and *** traditional fault managementsystems,this research has three-fold ***,this research leverages the ML and Deep Learning(DL)multi-classification system and evaluates their accuracy in detecting six distinct fault types,including fiber cut,fibereavesdropping,splicing,bad connector,bending,and PC ***,this paper assesses the classificationdelay of each classification ***,this work proposes a fiber optics fault prevention algorithm thatdetermines to mitigate the faults *** work utilized a publicly available fiber optics dataset namedOTDR_Data and applied different ML classifiers,such as Gaussian Naive Bayes(GNB),Logistic Regression(LR),Support Vector Machine(SVM),K-Nearest Neighbor(KNN),Random Forest(RF),and Decision Tree(DT).Moreover,Ensemble Learning(EL)techniques are applied to evaluate the accuracy of various *** addition,this work evaluated the performance of DL-based Convolutional Neural Network and Long-Short Term Memory(CNN-LSTM)hybrid *** findings reveal that the CNN-LSTM hybrid technique achieved the highestaccuracy of 99%with a delay of 360 *** the other hand,EL techniques improved the accuracy in detecting fiberoptic ***,this research comprehensively assesses accuracy and delay metrics for various classifiers andproposes the most efficient attack detection system in fiber optics.