Network Fault Severity Detection Through Log Data Analysis
DOI:
https://doi.org/10.52783/jns.v14.3621Keywords:
Log Data Analysis, CatBoost, Random Forest, XGBoost, LightGBM, Telecommunications NetworkAbstract
Network reliability is essential for faultless communication in the telecommunication sector. The paper, Network Fault Severity Detection Using Log Data, focuses on predicting fault severity in the network of Telstra based on machine learning. The research establishes a predictive model to predict fault severity into three categories: 1 denotes a few errors, 2 numerous faults in them, and 0 denotes no defects. The approach requires heavy data preprocessing, feature design, and data exploration to establish patterns in logs. Machine learning algorithms like CatBoost, Random Forest, XgBoost, and LightGBM are utilized for accurate prediction. Feature importance analysis is also used to further improve model explainability by isolating major factors of failure. The study highlights the predictive analytics contribution to enhancing network reliability, minimizing downtime, and maximizing customer satisfaction. The method aids Telstra and other telecommunication providers in optimizing service quality, resource efficiency, and maintenance. The scalable approach suggested guarantees proactive fault detection, thereby reducing operational costs and enhancing overall network performance.
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Saleem, Moeed, et al. "Machine learning for improved threat detection: lightGBM vs. CATBoost." Journal of Computing & Biomedical Informatics 7.01 (2024): 571-580.
[2] Tan, Ji Sheng, et al. "Predicting network faults using random forest and C5.0." International Journal of Engineering & Technology 7.2.14 (2018): 93-96.
[3] Boopathi, E., and V. Thiagarasu. "Edge detection in color images using RGB color model." Int J Comput Appl 180.9 (2018): 6-11.
[4] Ogar, Vincent Nsed, Sajjad Hussain, and Kelum AA Gamage. "Transmission line fault classification of multi-dataset using catboost classifier." Signals 3.3 (2022): 468-482.
[5] He, Shilin, et al. "A survey on automated log analysis for reliability engineering." ACM Computing Surveys (CSUR) 54.6 (2021): 1-37.
[6] Saied, Mohamed, Shawkat Guirguis, and Magda Madbouly. "A comparative study of using boosting-based machine learning algorithms for IoT network intrusion detection." International Journal of Computational Intelligence Systems 16.1 (2023): 177.
[7] Okokpujie, Kennedy, et al. "Development of a Machine Learning Based Fault Detection Model for Received Signal Level in Telecommunication Enterprise Infrastructure." International Journal of Safety & Security Engineering 14.3 (2024).
[8] Okey, Ogobuchi Daniel, et al. "BoostedEnML: Efficient technique for detecting cyberattacks in IoT systems using boosted ensemble machine learning." Sensors 22.19 (2022): 7409.
[9] Li, Shuai, et al. "Enhancing LightGBM for industrial fault warning: An innovative hybrid algorithm." Processes 12.1 (2024): 221.
[10] Lu, Zhiye, Lishu Wang, and Panbao Wang. "Microgrid Fault Detection Method Based on Lightweight Gradient Boosting Machine–Neural Network Combined Modeling." Energies 17.11 (2024): 2699.
[11] Rajalakshmi, D., et al. "Advancing Fault Detection Efficiency in Wireless Power Transmission with Light GBM for Real-Time Detection Enhancement." International Research Journal of Multidisciplinary Technovation 6.4 (2024): 54-68.
[12] Kumar, E. Boopathi, and M. Sundaresan. "Edge detection using trapezoidal membership function based on fuzzy's mamdani inference system." 2014 International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2014.
[13] Prakash, G., P. Logapriya, and A. Sowmiya. "Smart Parking System Using Arduino and Sensors." NATURALISTA CAMPANO 28 (2024): 2903-2911.
[14] Kumar, E. Boopathi, and V. Thiagarasu. "Comparison and Evaluation of Edge Detection using Fuzzy Membership Functions." International Journal on Future Revolution in Computer Science & Communication Engineering (IJFRCSCE), ISSN (2017): 2454-4248.
[15] Leevy, Joffrey L., et al. "Detecting cybersecurity attacks using different network features with lightgbm and xgboost learners." 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI). IEEE, 2020.
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