Abstract
Online anomaly detection in wireless sensor networks (WSNs) has been explored extensively. In this paper, exploiting the spatio-temporal correlation existed in the sensed data collected from WSNs, an online anomaly detector for WSNs are built based on ensemble learning theory. Considering the resources constrained in WSNs, ensemble pruning based on bio-geographical based optimization (BBO) is conducted. Experiments conducted on a real WSN dataset demonstrate that the proposed method is effective.
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References
Intel Berkely Reseach Lab (IBRL) dataset (2004), http://db.csail.mit.edu/labdata/labdata.html
Branch, J.W., Glannella, C., Szymanski, B., Wollf, R., Kargupta, H.: In-network Outlier Detection in Wireless Sensor Networks. Knowledge and Information Systems 34, 23–54 (2013)
Hejazi, M., Singh, Y.P.: One-class Support Vector Machines Approach to Anomay Detection. Applied Artificial Intelligence 27, 351–366 (2013)
Oza, N.C.: Online Bagging and Boosting, Systems, man and cybernetics. In: 2005 IEEE International Conference on, pp. 2340–2345 (2005)
Rassam, M.A., Zainal, A., Maarof, M.: An Adaptive and Efficient Dimension Reduction Model for Multivariate Wireless Sensor Networks Applications. Applied Soft Computing 13, 1978–1996 (2013)
Rassam, M.A., Zainal, A., Maarof, M.: One-Class Principal Component Classifier for Anomaly Detection in Wireless Sensor Network. In: 2012 Fourth International Conference on Computational Aspects of Social Networks, New York, pp. 271–276 (2012)
Simon, D.: Biogeography-based Optimization. IEEE Transactions on Evolutionary Computation 12, 702–713 (2008)
Xie, M., Hu, J., Han, S., Chen, H.: Scalable Hyper-Grid KNN-based Online Anomaly Detection in Wireless Sensor Networks. IEEE Transactions on Parallel and Distribution Systems 24, 1661–1670 (2012)
Zhang, Y.: Observing the unobservable: distributed online outlier detection in wireless sensor networks, p. 174. Universtiy of Twente, The Netherlands (2010)
Zhang, Y., Meratnia, N., Havinga, P.: Outlier Detection Techniques for Wireless Sensor Networks: A survey. IEEE Communications Surveys & Tutorials 12, 159–170 (2010)
Zhou, Z.H., Tang, W.: Selective Ensemble of Decision Trees, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pp. 476–483. Springer (2003)
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Ding, Z., Fei, M., Du, D., Xu, S. (2014). Online Anomaly Detection Method Based on BBO Ensemble Pruning in Wireless Sensor Networks. In: Ma, S., Jia, L., Li, X., Wang, L., Zhou, H., Sun, X. (eds) Life System Modeling and Simulation. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45283-7_17
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DOI: https://doi.org/10.1007/978-3-662-45283-7_17
Publisher Name: Springer, Berlin, Heidelberg
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