Abstract
Communication networks are inherently dynamic and the changes are often due to unpredicted causes, for instance, failures of the devices or bulks of user requests. To guarantee the continuation of the services, the providers should keep the typical activities of control and management of the network aligned with respect to these changes. They should handle both the evolution of the network and complexity of the infrastructure, while, actually, most of the existing technologies do not adopt update mechanisms or deal with the problem only for specific categories of networks. We propose a data mining approach to analyze evolving communication data while accounting for the whole network and its parts (devices and connections). The approach is able to detect changes that denote substantial and statistically evident variations in the communication modalities. Changes correspond to variations appearing in the frequent sub-networks discovered from evolving communication data: variations in the frequent sub-networks denote changes occurring in the raw data. We perform experimental evaluation on both real and synthetic networks and provide quantitative and qualitative results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bell, S., McDiarmid, A., Irvine, J.: Nodobo: Mobile phone as a software sensor for social network research. In: Proceedings of the 73rd IEEE Vehicular Technology Conference, VTC Spring 2011, 15–18 May 2011, Budapest, Hungary, pp. 1–5. IEEE (2011)
Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., Pedreschi, D.: Evolving networks: Eras and turning points. Intell. Data Anal. 17(1), 27–48 (2013)
Brauckhoff, D., Dimitropoulos, X.A., Wagner, A., Salamatian, K.: Anomaly extraction in backbone networks using association rules. IEEE/ACM Trans. Netw. 20(6), 1788–1799 (2012)
Ceci, M., Loglisci, C., Macchia, L.: Ranking sentences for keyphrase extraction: a relational data mining approach. Procedia Comput. Sci. 38, 52–59 (2014). https://doi.org/10.1016/j.procs.2014.10.011
Chakrabarti, D., Faloutsos, C.: Graph Mining: Laws, Tools, and Case Studies. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool Publishers, San Rafael (2012)
Cheng, H., Tan, P., Potter, C., Klooster, S.A.: A robust graph-based algorithm for detection and characterization of anomalies in noisy multivariate time series. In: ICDM Workshops, pp. 349–358. IEEE Computer Society (2008)
Gama, J., Gaber, M.M.: Learning from Data Streams: Processing Techniques in Sensor Networks. Springer, Berlin (2007)
He, W., Hu, G., Zhou, Y.: Large-scale IP network behavior anomaly detection and identification using substructure-based approach and multivariate time series mining. Telecommun. Syst. 50(1), 1–13 (2012)
Kim, T., Cho, S.: Web traffic anomaly detection using C-LSTM neural networks. Expert Syst. Appl. 106, 66–76 (2018)
Koh, Y.S.: CD-TDS: change detection in transactional data streams for frequent pattern mining. In: 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, BC, Canada, July 24–29, 2016, pp. 1554–1561 (2016)
Loglisci, C., Ceci, M., Impedovo, A., Malerba, D.: Mining microscopic and macroscopic changes in network data streams. Knowl.-Based Syst. 161, 294–312 (2018)
Loglisci, C., Ceci, M., Malerba, D.: Discovering evolution chains in dynamic networks. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) First International Workshop, NFMCP 2012, ECML/PKDD 2012, UK, 2012, Revised Selected Papers, Lecture Notes in Computer Science, vol. 7765, pp. 185–199. Springer (2012). https://doi.org/10.1007/978-3-642-37382-4_13
Loglisci, C., Malerba, D.: Leveraging temporal autocorrelation of historical data for improving accuracy in network regression. Stat. Anal. Data Min. 10(1), 40–53 (2017)
Nohuddin, P.N.E., Coenen, F., Christley, R., Setzkorn, C., Patel, Y., Williams, S.: Finding “interesting” trends in social networks using frequent pattern mining and self organizing maps. Knowl.-Based Syst. 29, 104–113 (2012)
Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly detection in dynamic networks: a survey. Wiley Interdiscip. Rev. Comput. Stat. 7(3), 223–247 (2015)
Sanctis, M.D., Bisio, I., Araniti, G.: Data mining algorithms for communication networks control: concepts, survey and guidelines. IEEE Netw. 30(1), 24–29 (2016)
Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Boston (2005)
Tran, D., Gaber, M.M., Sattler, K.: Change detection in streaming data in the era of big data: models and issues. SIGKDD Explor. 16(1), 30–38 (2014)
Wang, H., Tang, M., Park, Y., Priebe, C.E.: Locality statistics for anomaly detection in time series of graphs. IEEE Trans. Signal Process. 62(3), 703–717 (2014)
Wang, Y., Chakrabarti, A., Sivakoff, D., Parthasarathy, S.: Fast change point detection on dynamic social networks. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19–25, 2017, pp. 2992–2998 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Impedovo, A., Loglisci, C., Ceci, M., Malerba, D. (2020). Exploiting Pattern Set Dissimilarity for Detecting Changes in Communication Networks. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) Complex Pattern Mining. Studies in Computational Intelligence, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-36617-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-36617-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36616-2
Online ISBN: 978-3-030-36617-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)