Abstract
With the development of new networks, applications in mobile communication networks, such as mobile payment and online classes, have become an indispensable part of people’s lives. The core network is one of the most important components of the mobile communication network, which is not only essential but also complex. If the core network fails, it will cause a substantial economic loss. To ensure the reliability and stability of the mobile core network, operators need to detect abnormalities in Key Performance Indicators (KPI,e.g., average response time). Datasets of KPI are usually unbalanced and have a wide range of features. Therefore, we propose a correlation analysis-based KPI anomaly detection via an ensemble learning frame. This frame first performs data augmentation on the dataset using SMOTE oversampling algorithm and uses the Pearson correlation coefficient method for feature selection, then construct an ensemble learning XGBoost-based anomaly detection method for KPI. Finally, we evaluate our scheme with the confusion matrix. The results show that our scheme obtained a high accuracy and recall rate. The training and testing dataset we collected is a KPI dataset of a Chinese operator for the first three months of 2020. It is worth noting that no relevant studies used this dataset before.
Supported by organization ZTE industry-university research cooperation fund project “Research on network identity trusted communication technology architecture,” and State Key Laboratory of Mobile Network and Mobile Multimedia Technology, and Fundamental Research Funds under Grant 2021JBZD204.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
China Education Times. http://www.edu-gov.cn/edu/6547.html. Accessed 17 Jan 2020
Pei, D.: A study on KPI anomaly detection of cloud data center servers based on machine learning, Zhengzhou University (2020)
Sun, Y.Q., et al.: Evaluation of KPI anomaly detection methods. Front. Data Comput. Dev. 4(03), 46–65 (2022)
Zhang, Y., et al.: Network anomography. In: Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement, p. 30 (2005)
Siffer, A., et al.: Anomaly detection in streams with extreme value theory. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1067–1075 (2017)
Li, J., et al.: FluxEV: a fast and effective unsupervised framework for time-series anomaly detection. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 824–832 (2021)
Chen, Y., et al.: A provider-side view of web search response time. ACM SIGCOMM Comput. Commun. Rev. 43(4), 243–254 (2013)
Vallis, O., et al.: A novel technique for long-term anomaly detection in the cloud. In: 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 2014), Philadelphia, USA, pp. 534–537 (2014)
Pei, D., et al.: Intelligent operation and maintenance based on machine learning. Commun. Chin. Comput. Soc. 13(12), 68–72 (2017)
Liu, W.C., et al.: Artificial intelligence rehabilitation evaluation and training system for the degeneration of joint disease. ZTE Commun. 19(3), 46–55 (2021). https://doi.org/10.12142/ZTECOM.202103006
Liu, D.P., et al.: Opprentice: towards practical and automatic anomaly detection through machine learning. In: Proceedings of the Internet Measurement Conference 2015, pp. 211–224 (2015)
Laptev, N., et al.: Generic and scalable framework for automated time-series anomaly detection. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, pp. 1939–1947 (2015)
Yu, G., et al.: Unsupervised online anomaly detection with parameter adaptation for KPI abrupt changes. IEEE Trans. Netw. Serv. Manag. 17(3), 1294–1308 (2020)
Hu, M., et al.: Detecting anomalies in time series data via a meta-feature based approach. IEEE Access 6, 27760–27776 (2018)
Jia, M., et al.: DDoS attack detection method for space-based network based on SDN architecture. ZTE Commun. 18(4), 18–25 (2020). https://doi.org/10.12142/ZTECOM.202004004
Li, Y., et al.: IEEE EUC. Achieving a Blockchain-Based Privacy-Preserving Quality-Aware Knowledge Marketplace in Crowdsensing. Wuhan, China (2022)
Xu, H.W., et al.: Unsupervised anomaly detection via variational autoencoder for seasonal KPIs in web applications In: Proceedings of the 2018 World Wide Web Conference (2018)
Chen, W., et al.: Unsupervised anomaly detection for intricate KPIs via adversarial training of VAE. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, pp. 891–1899 (2019)
Zhang, W., et al.: Optimizing federated learning in distributed industrial IoT: a multi-agent approach. IEEE J. Sel. Areas Commun. 39(12), 3688–3703 (2021). https://doi.org/10.1109/JSAC.2021.3118352
Zhang, C., et al.: FRUIT: a blockchain-based efficient and privacy-preserving quality-aware incentive scheme. IEEE J. Sel. Areas Commun. Early Access (2022)
Zhang, W., et al.: Deep reinforcement learning based resource management for DNN inference in industrial IoT. IEEE Trans. Veh. Technol. 1 (2021)
Wang, J.S., et al.: ReLFA: resist link flooding attacks via Renyi entropy and deep reinforcement learning in SDN-IoT. China Commun. 19(7), 15 (2022)
Chawla, N.V., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Abdi, L., Hashemi, S.: To combat multi-class imbalanced problems by means of over-sampling techniques. IEEE Trans. Knowl. Data Eng. 28(1), 238–251 (2015)
Chen, R., et al.: Looseness diagnosis method for a connecting bolt of fan foundation based on sensitive mixed-domain features of excitation-response and manifold learning. Neurocomputing, p. S0925231216310694 (2016)
Xue, X., Zhou, J.: A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery. ISA Trans. 66, 284–295 (2016)
Chen, T.C., Guestrin, C. XGBoost: a scalable tree boosting system. In: The 22nd ACM SIGKDD International Conference (2016)
Wang, J., et al.: A review of feature selection methods. Comput. Eng. Sci. 12, 72–75 (2005)
Yıldız, O.T., Aslan, Ö., Alpaydın, E.: Multivariate statistical tests for comparing classification algorithms. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 1–15. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25566-3_1
Dong, M.G., et al.: A multi-class unbalanced oversampling algorithm using sampling safety factor. Comput. Scie. Explor. 14(10), 1776–1786 (2020)
Zhu, T.F., et al.: Synthetic minority oversampling technique for multiclass imbalance problems. Pattern Recogn. J. Pattern Recogn. Soc. 72, 327–340 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, L., Liu, Y., Zhang, W., Yan, X., Zhou, N., Jiang, Z. (2023). A Correlation Analysis-Based Mobile Core Network KPI Anomaly Detection Method via Ensemble Learning. In: Quan, W. (eds) Emerging Networking Architecture and Technologies. ICENAT 2022. Communications in Computer and Information Science, vol 1696. Springer, Singapore. https://doi.org/10.1007/978-981-19-9697-9_39
Download citation
DOI: https://doi.org/10.1007/978-981-19-9697-9_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9696-2
Online ISBN: 978-981-19-9697-9
eBook Packages: Computer ScienceComputer Science (R0)