Author:
Al Mamun S M Abdullah,Valimaki Juha
Reference18 articles.
1. Liu, D., Zhao, Y., Xu, H., Sun, Y., Pei, D., Luo, J., … Feng, M. (2015). Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning. In Proceedings of the 2015 Internet Measurement Conference on (pp. 211–224).
2. Bouillard, A., Junier, A., & Ronot, B. (2012). Hidden anomaly detection in telecommunication networks. In 2012 8th international conference on network and service management (cnsm) and 2012 workshop on systems virtualiztion management (svm) (pp. 82–90).
3. Qiu, H., Liu, Y., Subrahmanya, N. A., & Li, W. (2012). Granger Causality for Time-Series Anomaly Detection. In 2012 IEEE 12th International Conference on Data Mining (pp. 1074–1079)
4. Ciocarlie, G. F., Lindqvist, U., Nitz, K., Nováczki, S., & Sanneck, H. (2014). On the feasibility of deploying cell anomaly detection in operational cellular networks. In 2014 IEEE Network Operations and Management Symposium (NOMS) (pp. 1–6).
5. Ciocarlie, G. F., Lindqvist, U., Nitz, K., Nováczki, S., & Sanneck, H. (2014). DCAD: Dynamic Cell Anomaly Detection for operational cellular networks. In 2014 IEEE Network Operations and Management Symposium (NOMS) (pp. 1–2).
Cited by
19 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献