ANOMALY DETECTION IN COMPLEX NETWORK USING COMMUNITY DETECTION CONCEPT

Authors

  • Azadeh Oliyaei Department of Industrial Engineering, PhD Candidate, Iran University of Science and Technology, Tehran, Iran
  • Dr. Alireza Aliahmadi Department of Industrial Engineering, Faculty of Engineering, Iran University of Science and Technology, Tehran, Iran

Keywords:

Anomaly Detection, Social Network, Anomalous Users, Community Detection

Abstract

Discovering anomalous users in the social network is a crucial problem in analyzing network. The previous works focus on a network with just one type of interaction among the entities. However, the relationship among people is highly complex, and users have multiple types of interaction in a social network. On the other hand, users tend to form a community in the social network such that normal users usually have friends who are frends themselves, and anomalous users typically do not follow this rule. In this paper, we consider the detection of anomalous nodes in the multi-layer social network by combing the information in each layer of the network. We propose a pioneering algorithm based on the community detection method and assign the anomaly score to each user and rank them. Experimental result on real dataset shows that the proposed algorithm can recognize anomalous users in the multi-layer social network.

References

I. Aggarwal, CC., Zhao, Y., and Philip, SY. (2011) ' Outlier detection in graph streams', 2011 IEEE 27th International Conference on Data Engineering, pp. 399–409.

II. Aqib Javad, M., Younis , MH., Latif, S., Qadir, J., and Baig, A. (2018) ' Community detection in networks: A multidisciplinary review', Journal of Network and Computer Applications, pp. 87–111.

III. Akoglu, L., Glohon, M., and Faloutsos, C. (2010) ' Oddball: Spotting anomalies in weighted graphs' Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 410–421.

IV. Arab, M and Afsharchi, M. (2014) 'Community detection in social networks using hybrid merging of sub-communities', Journal of Network and Computer Applications, pp. 73-84

V. "Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., (2013a) 'Multidimensional networks: foundations of structural analysis' World Wide Web, pp. 567–593"

VI. Berlingerio, M., Coscia, M., and Giannotti, F., (2011b) 'Finding and characterizing communities in multidimensional networks' Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on, IEEE, pp. 490–494.

VII. Bindu, P.V., Thilagam, S., and Ahuja, D., (2017) ' Discovering suspicious behavior in multilayer social networks' Computers in Human Behavior, pp. 568-582.

VIII. Bindu, P.V., and Thilagam, S., (2016) ' Discovering suspicious behavior in multilayer social networks' Journal of Network and Computer Applications, pp. 213-229.

IX. Chandola, V., Banerjee, A., and Kumar, V., (2009) 'Anomaly detection: A survey' ACM Computing Surveys (CSUR), pp. 41-15.

X. Chandola, V., Banerjee, A., and Kumar, V., (2012) 'Anomaly detection for discrete sequences: A survey' Knowledge and Data Engineering, IEEE Transactions on Knowledge and Data Engineering, pp.823–839.

XI. Chunaev, P., (2020) 'Community detection in node-attributed social networks: A survey' Computer Science Review.

XII. Savage, D., Zhang, X., Yu, X., Chou, P., and Wang, Q., (2014) 'Anomaly Detection in Online Social Networks' Social Networks, pp. 62-70.

XIII. Debnath, S., Sarkar, D., and Jana, P., (2020).' Top-k Influential Nodes Identification Based on Activity Behaviors in Egocentric Online Social Networks'. Computational Intelligence in Pattern Recognition, pp. 463-475.

XIV. De Domenico, M., Lancichinetti, A., Arenas, A., and Rosvall, M., (2015) ' Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems' Phys. Rev.

XV. Diesner, J., Frantz, T.L., and Carley, K.M. (2005) 'Communication networks from the Enron email corpus “It's always about the people. Enron is no different', Computational & Mathematical Organization Theory.

XVI. Gao, J., Liang, F., Fan, W., Wang, C., Sun, Y., and Han, J., (2010) 'On community outliers and their efficient detection in information networks', in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM. pp. 813–822.

XVII. Hassanzadeh, R., Nayak, R., and Stebila, D., (2012) 'Analyzing the activeness of Graph Metrics for Anomaly Detection in Online Social Networks' International Conference on Web Information Systems Engineering, pp. 624–630.

XVIII. Hassanzadeh, R., and Nayak, R., (2013a) 'A rule-based hybrid method for anomaly detection in online-social-network graphs' 25th International Conference on, IEEE. pp. 351–357.

XIX. Hassanzadeh, R., and Nayak, R., (2013b) 'A semi-supervised graph-based algorithm for detecting outliers in online-social-networks' Proceedings of the 28th Annual ACM Symposium on Applied Computing, ACM. pp. 577-582.

XX. Hawkins, D.M., (1980) 'Identification of outliers.

XXI. Ji, T., Yang, D., and Gao, J., (2013) 'Incremental local evolutionary outlier detection for dynamic social networks' Proceedings, Part II, of the European Conferenceon Machin Learning and Knowledge Discovery in Databases, pp. 1–15.

XXII. Kunpeng, Z., Liang, Z., Zijian, L., and Ning, J., (2020) ' A deep learning based multitask model for network-wide traffic speed prediction' Neurocomputing, pp. 438–450.

XXIII. Kivel¨a, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., and Porter, M.A., (2014) 'Multilayer networks'.

XXIV. Lia, Y., Zhou, K., Lin, S., and Lo, N., (2019) ' F1ow-based Anomaly Detection Using Multilayer Perceptron in Software Defined Networks' IEEE Pervasive Computing, pp. 36–45.

XXV. Madan, A., Cebrian, M., Moturu, S., Farrahi, K., and Pentland, A., (2012) 'Sensing the” health state” of a community' IEEE Pervasive Computing, pp. 36–45.

XXVI. Manimaran, A., Chandramohan, D., Shrinivas. S.G., and Arulkumar, N., (2020) 'A comprehensive novel model for network speech anomaly detection system using deep learning approach ' International Journal of Speech Technology, pp. 305-313.

XXVII. Miller,B.A., Arcolano,N., and Bliss,N.T., (2013) 'Efficient anomaly detection in dynamic, attributed graphs: Emerging phenomena and big data' Intelligence and Security Informatics (ISI), 2013 IEEE International Conference on, IEEE. pp. 179–184.

XXVIII. Miller,B.A., Arcolano,N., and Bliss,N.T., (2013) 'Efficient anomaly detection in dynamic, attributed graphs: Emerging phenomena and big data' Intelligence and Security Informatics (ISI), 2013 IEEE International Conference on, IEEE. pp. 179–184.

XXIX. Muller, E., S´anchez, P.I., Mulle, Y., and Bohm, K., (2013) 'Ranking outlier nodesin subspaces of attributed graphs' Data Engineering Workshops (ICDEW), 2013 IEEE 29th International Conference on, IEEE. pp. 216–222.

XXX. Nanavati, A.A., Gurumurthy, S., Das, G., Chakraborty, D., Dasgupta, K., Mukherjea, S., and Joshi, A., (2006) 'On the structural properties of massive telecom call graphs: findings and implications' Proceedings of the 15th ACM international conference on Information and knowledge management. ACM, pp. 435–444.

XXXI. Roberts, N., Everton, S.F., (2011) 'Roberts and everton terrorist data: Noordin top terrorist network (subset)'.

XXXII. Savage, D., Zhang, X., Yu, X., Chou, P., and Wang, Q., (2014) 'Anomaly Detection in Online Social Networks' Social Networks, pp. 62-70.

XXXIII. Song, C., Liu, W., Liu, Z ., and Liu, X., (2019) ' User abnormal behavior recommendation via multilayer network'.

XXXIV. Sun, H., Huang, J., Han, J., Deng, H., Zhao, P., and Feng, B., (2010) 'gskeletonclu: Density-based network clustering via structure-connected tree division or agglomeration' Data Mining (ICDM) ,2010 IEEE10th International Conference on, IEEE, pp. 481–490.

XXXV. Sun, J., Qu, H., Chakrabarti, D., and Faloutsos, C., (2005) 'Neighborhood formation and anomaly detection in bipartite graphs' Data Mining, Fifth IEEE International Conference on, IEEE.

XXXVI. Ullah, W., Ullah, A., Haq, L., Muhammad, K., Sajjad M., and Baik, S., (2020) 'CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks'.

XXXVII. Tong, H., and Lin, C.Y., (2011) 'Non-Negative Residual Matrix Factorization with Application to Graph Anomaly Detection' SDM. SIAM, pp. 143–153.

XXXVIII. Xu, X., Yuruk, N., Feng, Z., and Schweiger, T.A., (2007) 'Scan: a structural clustering algorithm for networks' Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 824–833.

XXXIX. Yang, W., Shen, G.W., Wang, W., Gong, L.Y., Yu, M., and Dong, G.Z., (2015) 'Anomaly detection in microblogging via co-clustering' Journal of Computer Science and Technology, pp.1097–1108.

XL. Zhang, S., Xiao, K., Carranaz, E., Yang, F., and Zhoa, Z., (2019) Integration of auto-encoder network with density-based spatial clustering for geochemical anomaly detection for mineral exploration'. Computers & Geosciences, pp.43-56.

XLI. Zoppi, T., Ceccarelli., and Bondavalli, A., (2020) 'MADneSs: a Multi-layer Anomaly Detection Framework for Complex Dynamic Systems'. IEEE Transactions.

Additional Files

Published

15-10-2021

How to Cite

Azadeh Oliyaei, & Dr. Alireza Aliahmadi. (2021). ANOMALY DETECTION IN COMPLEX NETWORK USING COMMUNITY DETECTION CONCEPT. International Education and Research Journal (IERJ), 7(10). Retrieved from https://ierj.in/journal/index.php/ierj/article/view/2365

Issue

Vol. 7 No. 10 (2021): OCTOBER ISSUE, INTERNATIONAL EDUCATION AND RESEARCH JOURNAL

Section

Articles

License

Copyright (c) 2022 International Education and Research Journal (IERJ)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.