Abstract
With new network applications quickly appearing in power telecommunication access networks, Network traffic exhibits new abnormal behaviors. How to find out the abnormal traffic parts is very difficult. This paper bring forth a new anomaly detection approach to network traffic. Firstly, we take network traffic in power telecommunication access networks as a time series. Secondly, the factor analysis method is used to describe them. According to the factor decomposed theory, network traffic is divided into different factor components. Thirdly, the empirical mode decomposition is carried out for these two components. In this case, a quick anomaly detection algorithm is presented. Simulation results show that our approach is feasible and promising.
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
Jiang, D., Xu, Z., Zhang, P., et al.: A transform domain-based anomaly detection approach to network-wide traffic. J. Netw. Comput. Appl. 40(2), 292–306 (2014)
Aparicio-Navarro, F.J., Chambers, J.A., Kyriakopoulos, K., et al.: Using the pattern-of-life in networks to improve the effectiveness of intrusion detection systems. In: Proceedings of the ICC 2017, pp. 1–7 (2017)
Yuan, Z., Jiang, D., Tang, Q., et al.: A time-frequency analysis-based detection algorithm for network traffic anomaly. In: Proceedings of the COIN 2013, pp. 1–4 (2013)
Xiong, W., Hu, H., Xiong, N., et al.: Anomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communications. Inf. Sci. 2014(258), 403–415 (2014)
Jiang, D., Yao, C., Xu, Z., et al.: Multi-scale anomaly detection for high-speed network traffic. Trans. Emerg. Telecommun. Technol. 26(3), 308–317 (2015)
Wang, Z., Yang, J., Zhang, S. et al.: Robust regression for anomaly detection. In: Proceedings of the ICC 2017, pp. 1–6 (2017)
Jiang, D., Yuan, Z., Zhang, P., et al.: A traffic anomaly detection approach in communication networks for applications of multimedia medical devices. Multimed. Tools Appl. 75(22), 1–25 (2016)
Thatte, G., Mitra, U., Heidemann, J.: Parametric methods for anomaly detection in aggregate traffic. IEEE Trans. Netw. 19(2), 512–525 (2011)
Jiang, D., Qin, W., Nie, L., et al.: Time-frequency detection algorithm of network traffic anomalies. In: Proceedings of the ICIIM 2012, pp. 1–4 (2012)
Thatte, G., Mitra, U., Heidemann, J.: Parametric methods for anomaly detection in aggregate traffic. IEEE/ACM Trans. Netw. 19(2), 512–525 (2011)
Eriksson, B., Barford, P., Bowden, R., et al.: Basisdetect: a model-based network event detection framework. In: Proceedings of the IMC, pp. 451–464 (2010)
Jiang, D., Yao, C., Zhang, W., et al.: A detection algorithm to anomaly network traffic based on spectral kurtosis analysis. In: Proceedings of the ITSim 2013, pp. 980–983 (2013)
Jiang, D., Nie, L., Lv, Z., et al.: Spatio-temporal Kronecker compressive sensing for traffic matrix recovery. IEEE Access 4, 3046–3053 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ji, P. et al. (2018). A Factor Analysis-Based Detection Approach to Network Traffic Anomalies for Power Telecommunication Access Networks. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_9
Download citation
DOI: https://doi.org/10.1007/978-981-13-0893-2_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0892-5
Online ISBN: 978-981-13-0893-2
eBook Packages: Computer ScienceComputer Science (R0)