Abstract
Over the last decade, there has been significant interest in detecting hybrid anomalies in Online Social Networks (OSN). However, there remain several questions regarding the evaluation of these systems and the nature of anomaly. In order to answer these questions, further research must be conducted on anomaly definitions and development for social networks. This is achieved through datasets that represent a balanced testing environment and contains anomalies with selected characteristics. In this paper, we propose an artificial injection agent as a security solution for evaluating detecting users’ abnormal behavior in OSN. The proposed agent creates synthetic anomaly and injects it into the data to utilize the detection accuracy in a Bayesian Network Classifier. We evaluated the effectiveness of the proposed technique concerning the detection of the injected anomalies. Using our approach, we were able to detect the injected anomalies with a success rate of 94% and no false alarm rates.
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References
Brauckhoff, D., Wagner, A., May, M.: FLAME: A Flow-Level Anomaly Modeling Engine. In: CSET (2008)
Hawkins, D.M.: Identification of Outliers, vol. 11. Chapman and Hall, London (1980)
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)
Zhang, J., Paschalidis, I.C.: Statistical anomaly detection via composite hypothesis testing for Markov models. IEEE Trans. Sig. Process. 66(3), 589–602 (2018)
Wazid, M., Das, A.K.: An efficient hybrid anomaly detection scheme using K-means clustering for wireless sensor networks. Wirel. Pers. Commun. 90(4), 1971–2000 (2016). https://doi.org/10.1007/s11277-016-3433-3
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)
Wallschläger, M., Gulenko, A., Schmidt, F., Kao, O., Liu, F.: Automated anomaly detection in virtualized services using deep packet inspection. Procedia Comput. Sci. 110, 510–515 (2017)
Kumar, S.: Classification and detection of computer intrusions. ProQuest Dissertations Publishing (1995)
Kumar, A., Saradhi, V.V., Venkatesh, T.: Network-wide volume anomaly detection using alternate matrix decomposition techniques. In: 2017 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE (2017)
Eberle, W., Holder, L.: Anomaly detection in data represented as graphs. Intell. Data Anal. 11(6), 663–689 (2007)
Priebe, C.E., et al.: Statistical inference on attributed random graphs: fusion of graph features and content: an experiment on time series of Enron graphs. Comput. Stat. Data Anal. 54(7), 1766–1776 (2010)
Zhang, Y., Debroy, S., Calyam, P.: Network-wide anomaly event detection and diagnosis with perfSONAR. IEEE Trans. Netw. Serv. Manage. 13(3), 666–680 (2016)
Lu, Y., et al.: An unsupervised approach to anomaly detection in music datasets. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM (2016)
Hochenbaum, J., Vallis, O.S., Kejariwal, A.: Automatic anomaly detection in the cloud via statistical learning. arXiv preprint arXiv:1704.07706 (2017)
Grinstein, G., Plaisant, C., Laskowski, S., O’Connell, T., Scholtz, J., Whiting, M.: VAST 2008 challenge: introducing mini-challenges. In: IEEE Symposium on Visual Analytics Science and Technology, VAST’08, 19 October 2008, pp. 195–196. IEEE (2019)
Heard, N.A., Weston, D.J., Platanioti, K., Hand, D.J.: Bayesian anomaly detection methods for social networks. Ann. Appl. Stat. 4(2), 645–662 (2010)
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Aljably, R., Al-Rodhaan, M., Tian, Y. (2022). Detecting Hybrid Anomalies Using an Unsupervised Approach in Online Social Networks. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_50
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DOI: https://doi.org/10.1007/978-981-19-0852-1_50
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