Abstract
This chapter deals with an important analysis task over dynamic networks, namely exploring the time varying characteristics of anomalies present in such networks. In this direction, a graph mining based framework is considered that takes a sequence of network snapshots as input for analysis. It defines various categories of temporal anomalies typically encountered in such an exploration and characterizes them appropriately to enable their detection. An experimental study of this framework over benchmark graph data sets is presented here to demonstrate the evolving behavior of the anomalies detected as per the categories defined.
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
Akoglu, L., McGlohon, M., Faloutsos, C.: Oddball: spotting anomalies in weighted graphs. In: 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD), pp. 410–421. Hyderabad, India (2010)
Anagnostopoulos, A., Kumar, R., Mahdian, M., Upfal, E., Vandin, F.: Algorithms on evolving graphs. In: ACM ITCS. Cambridge, Massachussets, USA (2012)
Bridges, R.A., Collins, J.P., Ferragut, E.M., Laska, J.A., Sullivan, B.D.: Multi-level anomaly detection in time-varying graph data. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM, pp. 579–583. ACM, Paris, France (2015)
Chakrabarti, D.: Autopart: parameter-free graph partitioning and outlier detection. In: PKDD, pp. 112–124 (2004)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3) (2009)
Eberle, W., Holder, L.: Discovering structural anomalies in graph-based data. In: IEEE ICDM Workshops, pp. 393–398 (2007)
Eberle, W., Holder, L.: Streaming data analytics for anomalies in graphs. In: 2015 IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, Waltham, USA, pp. 1–6 (2015)
Gupta, M., Gao, J., Aggarwal, C.C., Han, J.: Outlier Detection for Temporal Data. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool Publishers (2014)
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, M., Leskovec, J.: Latent multi-group memebership graph model. In: 29th International Conference on Machine Learning (ICML). Edinburgh, Scotland, UK (2012)
Leskovec, J., Krevl, A.: SNAP Datasets: stanford large network dataset collection. http://snap.stanford.edu/data (2014)
Ley, M.: DBLP—some lessons learned. In: PVLDB, vol. 2, issue 2, pp. 1493–1500 (2009)
Li, X., Bian, F., Crovella, M., Diot, C., Govindan, R., Iannaccone, G., Lakhina, A.: Detection and identification of network anomalies using sketch subspaces. In: ACM SIGCOMM Conference on Internet Measurement Conference (IMC), Rio de Janeiro, Brazil, pp. 147–152 (2006)
Linked stream benchmark data generator. http://code.google.com/p/lsbench
Mitra, S., Bagchi, A.: Modeling temporal variation in social network: an evolutionary web graph approach. In: B. Furht (ed.) Handbook of Social Network Technologies, pp. 169–184. Springer (2010)
Mongiovi, M., Bogdanov, P., Ranca, R., Singh, A.K., Papalexakis, E.E., Faloutsos, C.: Netspot: spotting significant anomalous regions on dynamic networks. In: SDM. Austin, Texas (2013)
Noble, C.C., Cook, D.J.: Graph-based anomaly detection. In: SIGKDD, Washington, DC, USA, pp. 631–636 (2003)
Ohnishi, K., Koppen, M., Yoshida, K.: Evolutionary linkage creation between information sources in P2P networks. Evol. Intell. 5(4), 245–259 (2012)
Papalexakis, E.E., Akoglu, L., Ienco, D.: Do more views of a graph help? community detection and clustering in multi-graphs. In: Fusion. Istanbul, Turkey (2013)
Rossi, R.A., Neville, J., Gallagher, B., Henderson, K.: Modeling dynamic behavior in large evolving graphs. In: WSDM. Rome, Italy (2013)
Suri, N.N.R.R., Murty, M.N., Athithan, G.: Characterizing temporal anomalies in evolving networks. In: 18th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Part-I, vol. LNAI 8443, pp. 422–433. Springer, Switzerland, Tainan, Taiwan (2014)
Suri, N.N.R.R., Murty, M.N., Athithan, G.: Data mining techniques for outlier detection. In: Q. Zhang, R.S. Segall, M. Cao (eds.) Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications, chap. 2, pp. 22–38. IGI Global, New York, USA (2011)
Thottan, M., Ji, C.: Anomaly detection in IP networks. IEEE Trans. Signal Process. 51(8), 2191–2204 (2003)
Toahchoodee, M., Ray, I., McConnell, R.M.: Using graph theory to represent a spatio-temporal role-based access control model. Int. J. Next Gener. Comput. 1(2) (2010)
Yu, W., Aggarwal, C.C., Ma, S., Wang, H.: On anomalous hotspot discovery in graph streams. In: ICDM (2013)
Zainal, A., Maarof, M.A., Shamsuddin, S.M., Abraham, A.: Ensemble of one-class classifiers for network intrusion detection system. In: The Fourth International Conference on Information Assurance and Security, IEEE Computer Society, Napoli, Italy, pp. 180–185 (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ranga Suri, N.N.R., Murty M, N., Athithan, G. (2019). Detecting Anomalies in Dynamic Networks. In: Outlier Detection: Techniques and Applications. Intelligent Systems Reference Library, vol 155. Springer, Cham. https://doi.org/10.1007/978-3-030-05127-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-05127-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05125-9
Online ISBN: 978-3-030-05127-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)