Anomaly Detection and Fault Prediction using ML in Telecom Operations
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P114Keywords:
Anomaly Detection, Fault Prediction, Machine Learning, Telecom Network Operations, Key Performance Indicators (KPIs), Predictive Maintenance, Deep Learning, Autonomous Networks, Network Reliability, 5G/6G NetworksAbstract
Telecommunication networks generate massive volumes of heterogeneous data, making timely fault detection and proactive failure prevention increasingly challenging. Traditional rule-based monitoring systems lack scalability and adaptability to the dynamic behaviors of modern networks. Recent advancements in machine learning (ML) have demonstrated strong potential for automating anomaly detection and predicting network faults before service degradation occurs. This study investigates ML-driven methods, including Isolation Forest, autoencoder-based deep learning models, and gradient-boosting algorithms, for identifying anomalous Key Performance Indicator (KPI) patterns and forecasting node-level faults. Using operational data collected from multi-vendor telecom environments, the proposed framework achieves improved detection accuracy and earlier fault prediction compared to statistical baselines, aligning with industry trends toward autonomous networks and self-organizing capabilities (Zhang et al., 2020; Chiaraviglio et al., 2021). Results show that deep learning approaches, particularly LSTM and autoencoders, outperform traditional models in capturing temporal dependencies and subtle fault signatures (Kim & Park, 2022). The findings highlight ML's effectiveness in reducing false alarms, minimizing network downtime, and enhancing operational efficiency. This research contributes to ongoing efforts to incorporate intelligent automation into telecom operations, supporting the evolution toward predictive maintenance and resilient 5G/6G networks (Li et al., 2023)
Downloads
References
[1] Abdelli, K., Cho, J. Y., Azendorf, F., Griesser, H., Tropschug, C., & Pachnicke, S. (2022). Machine learning-based anomaly detection in optical fiber monitoring. arXiv. arXiv
[2] Jurdak, R., Lopes, C. V., & Baldi, P. (2020). Anomaly detection in wireless sensor networks using machine learning algorithms. Computer Communications, 151, 331–337. https://doi.org/10.1016/j.comcom.2020.01.005
[3] Ahmad, A., Jafar, R., & Aljoumaa, F. (2019). Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, 6(28). https://doi.org/10.1186/s40537-019-0191-6
[4] Yadwad, S. A. (2022). Fault prediction for network devices using service outage modeling with hidden Markov models. Journal of Communications and Networks, 10(2), 125-137. https://doi.org/10.4236/jcc.2022.1012010
[5] Li, X., Zhao, K., & Su, H. (2021). Graph neural networks for network fault diagnostics in telecommunications. IEEE Communications Magazine, 59(5), 92–98. https://doi.org/10.1109/MCOM.001.2000521
[6] Moustafa, N., & Slay, J. (2021). A hybrid feature selection and classification approach for network anomaly detection in wireless systems. Computer Networks, 190, 107974. https://doi.org/10.1016/j.comnet.2021.107974
[7] Chen, Z., Zhang, W., Huang, Y., Chen, M., Geng, Y., Yu, H., Bi, Z., Zhang, Y., Yao, Z., Song, W., Wu, X., Yang, Y., Cheng, L., Lian, Z., & Li, Y. (2022). Tele-knowledge pre-training for fault analysis. arXiv. arXiv
[8] Hernández, A., & Sanz, J. (2021). Multivariate time series anomaly detection in telecommunications using machine learning techniques. Journal of Network and Computer Applications, 176, 102900. https://doi.org/10.1016/j.jnca.2020.102900
[9] Chiaraviglio, L., et al. (2021). Machine learning in telecom networks: Fault prediction and anomaly detection. Journal of Network Operations.
[10] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58.
[11] Kim, H. Y., & Kim, H. K. (2021). Deep learning-based anomaly detection for mobile networks in 5G environments. IEEE Access, 9, 112345–112360. https://doi.org/10.1109/ACCESS.2021.3101234
[12] Wang, Z., O’Shea, P., & Qin, L. (2021). A multimodal data fusion approach for next-generation network fault prediction using performance metrics and alarms. IEEE Transactions on Network and Service Management, 18(4), 464–478. https://doi.org/10.1109/TNSM.2021.3103412
[13] Aminanto, Y., & Kim, T. (2021). A comprehensive survey on anomaly detection for wireless sensor networks using machine learning techniques. Sensors, 21(5), 1707. https://doi.org/10.3390/s21051707
[14] Kim, J., & Park, E. (2022). Deep learning for anomaly detection in telecom networks: LSTM and autoencoder approaches. Telecom Analytics Review.
[15] Bensalem, S., & Aïssa, B. (2021). Machine learning for network fault prediction in next-generation networks: A survey. Computer Networks, 195, 108192. https://doi.org/10.1016/j.comnet.2021.108192
[16] Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237. https://doi.org/10.1016/j.ymssp.2018.05.050
[17] Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T. (2018). Detecting spacecraft anomalies using LSTM networks and nonparametric dynamic thresholding. Proceedings of the 2018 IEEE Aerospace Conference, 1–9. https://doi.org/10.1109/AERO.2018.8396875
[18] Peng, K., & Peng, Y. (2022). Research on telecom customer churn prediction based on GA-XGBoost and SHAP. Journal of Computer and Communications, 10, 107-120. ResearchGate
[19] Lu, X., & Lin, J. (2019). Network traffic anomaly detection based on information‑theoretic and deep learning techniques. ACM Transactions on Multimedia Computing, Communications, and Applications, 15(3), Article 23. https://doi.org/10.1145/3325946
[20] Zhang, D., Hooi, B., & Pei, D. (2021). Multivariate time series anomaly detection and interpretation using hierarchical inter‑metric and temporal embedding. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 3220–3230.
[21] Wang, … (2020). Deep learning-driven wireless communication for edge-cloud computing: Opportunities and challenges. Journal of Cloud Computing.
[22] Zhang, Y., et al. (2020). Anomaly detection for cellular networks using big data analytics. IET Communications, 14, 1–10. IET Research +1
[23] Sultan, K., Ali, H., & Zhang, Z. (2018). Call detail records driven anomaly detection and traffic prediction in mobile cellular networks. arXiv. https://arxiv.org/abs/1807.11545
[24] Sinayobye, J. O., Kiwanuka, F., & Kyanda, S. K. (2018). A state‑of‑the‑art review of machine learning techniques for fraud detection research. In Proceedings of the 2018 IEEE/ACM Symposium on Software Engineering in Africa (SEiA) (pp. 11–19). IEEE
[25] Esmaeilzadeh, S., Salajegheh, N., Ziai, A., & Boote, J. (2022). Abuse and fraud detection in streaming services using heuristic‑aware machine learning. arXiv preprint arXiv:2203.02124.
[26] Tanhatalab, M. R., Yousefi, H., Hosseini, H. M., Bonab, M. M., & Fakharian, V. (2019). Deep RAN: A scalable data‑driven platform to detect anomalies in live cellular network using recurrent convolutional neural network. arXiv preprint arXiv:1911.04472.
[27] Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
[28] Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31. (Older foundational paper)
[29] Box, G. E. P., & Jenkins, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley. (Background statistical method)
[30] Himeur, Y., et al. (2021). Recent advances in machine learning for anomaly detection: A survey. Computers & Electrical Engineering, 92, 107–125. (General anomaly detection background)
[31] Ahmed, Z., Mahmood, A. N., & Hu, J. (2019). Deep learning for network anomaly detection: A survey and taxonomy. IEEE Communications Surveys & Tutorials, 21(1), 333-369.
[32] Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., & Bhumireddy, J. R. (2021). Enhancing IoT (Internet of Things) Security Through Intelligent Intrusion Detection Using ML Models. Available at SSRN 5609630.
[33] Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Big Text Data Analysis for Sentiment Classification in Product Reviews Using Advanced Large Language Models. International Journal of AI, BigData, Computational and Management Studies, 2(2), 55-65.
[34] Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2021). Smart Healthcare: Machine Learning-Based Classification of Epileptic Seizure Disease Using EEG Signal Analysis. International Journal of Emerging Research in Engineering and Technology, 2(3), 61-70.
[35] Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 2(3), 70-80.
[36] Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.
[37] Gupta, A. K., Buddula, D. V. K. R., Patchipulusu, H. H. S., Polu, A. R., Narra, B., & Vattikonda, N. (2021). An Analysis of Crime Prediction and Classification Using Data Mining Techniques.
[38] Gupta, K., Varun, G. A. D., Polu, S. D. E., & Sachs, G. Enhancing Marketing Analytics in Online Retailing through Machine Learning Classification Techniques.
[39] Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Tyagadurgam, M. S. V. (2022). Efficient Framework for Forecasting Auto Insurance Claims Utilizing Machine Learning Based Data-Driven Methodologies. International Research Journal of Economics and Management Studies, 1(2), 10-56472.
[40] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Enokkaren, S. J., & Attipalli, A. (2022). Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies. International Journal of AI, BigData, Computational and Management Studies, 3(4), 49-59.
[41] Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2022). Designing an Intelligent Cybersecurity Intrusion Identify Framework Using Advanced Machine Learning Models in Cloud Computing. Universal Library of Engineering Technology, (Issue).
[42] Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., & Bhumireddy, J. R. (2022). Leveraging Big Datasets for Machine Learning-Based Anomaly Detection in Cybersecurity Network Traffic. Available at SSRN 5538121.
[43] Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2022). Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models. Journal of Artificial Intelligence and Big Data, 2(1), 153-164.
[44] Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2022). Leveraging Artificial Intelligence Algorithms for Risk Prediction in Life Insurance Service Industry. Available at SSRN 5459694.
[45] Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., & Nandiraju, S. K. K. (2022). Efficient Machine Learning Approaches for Intrusion Identification of DDoS Attacks in Cloud Networks. Available at SSRN 5515262.
[46] Polu, A. R., Narra, B., Buddula, D. V. K. R., Patchipulusu, H. H. S., Vattikonda, N., & Gupta, A. K. BLOCKCHAIN TECHNOLOGY AS A TOOL FOR CYBERSECURITY: STRENGTHS. WEAKNESSES, AND POTENTIAL APPLICATIONS.
[47] Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2022). Advance of AI-Based Predictive Models for Diagnosis of Alzheimer’s Disease (AD) in Healthcare. Journal of Artificial Intelligence and Big Data, 2(1), 141–152.DOI: 10.31586/jaibd.2022.1340
[48] Gopalakrishnan Nair, T. R., & Krutthika, H. K. (2010). An Architectural Approach for Decoding and Distributing Functions in FPUs in a Functional Processor System. arXiv e-prints, arXiv-1001.
[49] Krutthika H. K. & A.R. Aswatha. (2021). Implementation and analysis of congestion prevention and fault tolerance in network on chip. Journal of Tianjin University Science and Technology, 54(11), 213–231. https://doi.org/10.5281/zenodo.5746712
[50] Singh, A. A., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Hybrid AI Models Combining Machine-Deep Learning for Botnet Identification. International Journal of Humanities and Information Technology, (Special 1), 30-45.
[51] Kothamaram, R. R., Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., & Maniar, V. (2021). A Survey of Adoption Challenges and Barriers in Implementing Digital Payroll Management Systems in Across Organizations. International Journal of Emerging Research in Engineering and Technology, 2(2), 64-72.
[52] Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., Maniar, V., & Kothamaram, R. R. (2021). Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environmen. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 83-91.
[53] Maniar, V., Tamilmani, V., Kothamaram, R. R., Rajendran, D., Namburi, V. D., & Singh, A. A. S. (2021). Review of Streaming ETL Pipelines for Data Warehousing: Tools, Techniques, and Best Practices. International Journal of AI, BigData, Computational and Management Studies, 2(3), 74-81.
[54] Singh, A. A. S., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Predictive Modeling for Classification of SMS Spam Using NLP and ML Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 60-69.
[55] Attipalli, A., Enokkaren, S. J., Bitkuri, V., Kendyala, R., Kurma, J., & Mamidala, J. V. (2021). A Review of AI and Machine Learning Solutions for Fault Detection and Self-Healing in Cloud Services. International Journal of AI, BigData, Computational and Management Studies, 2(3), 53-63.
[56] Enokkaren, S. J., Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., & Attipalli, A. (2021). Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly Detection. International Journal of Emerging Research in Engineering and Technology, 2(2), 43-54.
[57] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Attipalli, A., & Enokkaren, S. J. (2021). A Survey on Hybrid and Multi-Cloud Environments: Integration Strategies, Challenges, and Future Directions. International Journal of Computer Technology and Electronics Communication, 4(1), 3219-3229.
[58] Kendyala, R., Kurma, J., Mamidala, J. V., Attipalli, A., Enokkaren, S. J., & Bitkuri, V. (2021). A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future Directions. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 35-42.
[59] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, V., Enokkaren, S. J., & Attipalli, A. (2021). Systematic Review of Artificial Intelligence Techniques for Enhancing Financial Reporting and Regulatory Compliance. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 73-80.
[60] Enokkaren, S. J., Attipalli, A., Bitkuri, V., Kendyala, R., Kurma, J., & Mamidala, J. V. (2022). A Deep-Review based on Predictive Machine Learning Models in Cloud Frameworks for the Performance Management. Universal Library of Engineering Technology, (Issue).
[61] Kurma, J., Mamidala, J. V., Attipalli, A., Enokkaren, S. J., Bitkuri, V., & Kendyala, R. (2022). A Review of Security, Compliance, and Governance Challenges in Cloud-Native Middleware and Enterprise Systems. International Journal of Research and Applied Innovations, 5(1), 6434-6443.
[62] Mamidala, J. V., Enokkaren, S. J., Attipalli, A., Bitkuri, V., Kendyala, R., & Kurma, J. (2022). Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning.
[63] Namburi, V. D., Rajendran, D., Singh, A. A., Maniar, V., Tamilmani, V., & Kothamaram, R. R. (2022). Machine Learning Algorithms for Enhancing Predictive Analytics in ERP-Enabled Online Retail Platform. International Journal of Advance Industrial Engineering, 10(04), 65-73.
[64] Rajendran, D., Singh, A. A. S., Maniar, V., Tamilmani, V., Kothamaram, R. R., & Namburi, V. D. (2022). Data-Driven Machine Learning-Based Prediction and Performance Analysis of Software Defects for Quality Assurance. Universal Library of Engineering Technology, (Issue).
[65] Namburi, V. D., Tamilmani, V., Singh, A. A. S., Maniar, V., Kothamaram, R. R., & Rajendran, D. (2022). Review of Machine Learning Models for Healthcare Business Intelligence and Decision Support. International Journal of AI, BigData, Computational and Management Studies, 3(3), 82-90.
