A Novel Ensemble Approach for Anomaly Detection in Wireless Sensor Networks Using Time-overlapped Sliding Windows

Document Type : Original Research (Full Papers)

Authors

Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data mining.  Our proposed algorithm, at first applies a fuzzy clustering approach using the well-known C-means clustering method to create the clusters. In the classification step, we created some base classifiers, each of which utilizes the data of overlapping windows to utilize the correlation among data over time by creating time-overlapped batches of data. By aggregating these batches, the classifier proceeds to find an appropriate label for future incoming instance. The concept of “Ensemble of Classifiers” with majority voting scheme has been used in order to combine the judgment of all classifiers. The results of our implementation with MATLAB toolboxes shows that the proposed majority-based ensemble learning method attains more efficiency compared to the case of the single classifier method. Our proposed method enhances the performance of the system in terms of major criteria such as False Positive Rate, True Positive Rate, False Negative Rate, True Negative Rate, Sensitivity, Specificity and also the ROC curve.

Keywords


[1] Malik, N.; Kumar, P., "Distributed Data Mining in Wireless Sensor Network Using Fuzzy Naïve Byes." International Journal of Engineering and Computer Science, vol. 6, no. 8, pp. 22327-22332 (2017).
[2] Guo, X.; Wang, D.; Chen, F., "An Anomaly Detection Based on Data Fusion Algorithm in Wireless Sensor Networks." International Journal of Distributed Sensor Networks, pp. 1-10 (2015).
[3] Tripathi, R.; Dwivedi, S. K., "A Quick Review of Data Stream Mining Algorithms." Imperial Journal of Interdisciplinary Research,vol. 2. No. 7, pp. 870-873 (2016).
[4] Ahmed, M.; Mahmood, A. N.; Hu, J., "A Survey of Network Anomaly Detection Techniques." Journal of Network and Computer Applications, vol. 60, pp. 19–31 (2016).
[5] Thuc, K.-X.; Insoo, K., "A Collaborative Event Detection Scheme Using Fuzzy Logic in Clustered Wireless Sensor Networks." AEUInternational Journal of Electronics and Communications, vol. 65,no. 5, pp. 485–488 (2011).
[6] Islam, R.; Shahadat Hossain, M.; Andersson, K., "A Novel Anomaly Detection Algorithm for Sensor Data under Uncertainty." Soft Computing, vol. 22, no.5, pp. 1623-1639 (2018).
[7] Gil, P.; Martins, H.; Januário, F., "Outliers Detection Methods in Wireless Sensor Networks." Artificial Intelligence Review,Springer, pp. 1-26 (2018).
[8] Zhang, Y.; Meratnia, N.; Havinga, P. J.M., "Distributed Online Outlier Detection in Wireless Sensor Networks Using Ellipsoidal Support Vector Machine." Ad Hoc Networks, vol. 11, no.3, pp.1062–1074 (2013).
[9] Knorr, E.; Ng, R.T., "Algorithms for Mining Distance-based Outliers in Large Data Sets." VLDB 1998. In: Proceedings of the 24th International Conference on Very Large Databases pp. 392-403.New York City, USA (1998).
[10] Araya, D. B.; Grolinger, K.; ElYamany, H. F.; Capretz, M. A.;Bitsuamlak, G., "An Ensemble Learning Framework for Anomaly Detection in Building Energy Consumption." Energy and Buildings,vol. 144, pp. 191-206 (2017).
[11] Zhang, J.; Gardner, R.; Vukotic, I., "Anomaly Detection in Wide Area Network Meshes Using Two Machine Learning Algorithms."Future Generation Computer Systems, in press, accepted manuscript, (2018).
[12] Zhou, Z.-H., Ensemble learning. Encyclopedia of Biometrics,Springer, Berlin, Germany, pp. 270–273 (2009).
[13] Ayadi, A.; Ghorbel, O.; Obeid, A. M.; Abid, M., "Outlier Detection Approaches for Wireless Sensor Networks: A Survey." Computer Networks, vol. 129, no. 1, pp. 319-333 (2017).
[14] Agrawal, S.; Agrawal, J., "Survey on Anomaly Detection Using Data Mining Techniques." Procedia Computer Science, vol. 60, pp.
708-713 (2015).
[15] Ghorbel, O.; Abid, M.; Snoussi, H., "Improved KPCA for Outlier Detection in Wireless Sensor Networks." ATSIP 2014, 1st International Conference on Advanced Technologies for Signal and Image Processing. Sousse, Tunisia, IEEE, pp. 507–511 (2014).
[16] Asmuss, J.; Lauks, G., "Network Traffic Classification for Anomaly Detection Fuzzy Clustering-based Approach." FSKD 2015. 12th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China. IEEE, pp. 313-318 (2017).
[17] Dromard, J.; Roudière, G.; Owezarski, P., "Online and Scalable Unsupervised Network Anomaly Detection Method." IEEE Transactions on Network and Service Management, vol. 14, no. 1,
pp. 34-47 (2017).
[18] Tan, P.N.; Steinbach, M.; Kumar, V., "Introduction to Data Mining." Addison-Wesley (2005).
[19] Bhargava, A.; Raghuvanshi, A., "Anomaly Detection in Wireless Sensor Network Using S-Transform in Combination with SVM."IEEE International Conference on Computational Intelligence and Communication Networks. Mathura, India, IEEE, pp. 111-116 (2013).
[20] Araya, D. B.; Grolinger, K.; ElYamany, H. F.; Capretz, M. A.;Bitsuamlak, G., "Collective Contextual Anomaly Detection Framework for Smart Buildings." IJCNN 2016. IEEE International
Joint Conference on Neural Networks. Vancouver, BC, Canada,IEEE, pp. 511–518 (2016).
[21] Ahmad, S.; Lavin, A.; Purdy, S.; Agha, Z., "Unsupervised Real-Time Anomaly Detection for Streaming Data." Neurocomputing,vol. 262, pp. 134-147 (2017).
[22] Padilla, D.E.; Brinkworth, R.; McDonnell, M.D., "Performance of a Hierarchical Temporal Memory Network in Noisy Sequence Learning." CYBERNETICSCOM 2013. Proceedings of the IEEE International Conference on Computational Intelligence and Cybernetics. Yogyakarta, Indonesia, IEEE, pp. 45-51 (2013).
[23] Dominguesa, R.; Filipponea, M.; Michiardia, P.; Zouaoui, J., "A Comparative Evaluation of Outlier Detection Algorithms:Experiments and Analyses." Pattern Recognition, Elsevier, vol. 74,
pp. 406-421 (2018).
[24] Bosman, H. H.; Iacca, G.; Tejada, A.; Wörtche, H. J.; Liotta, A.,"Spatial Anomaly Detection in Sensor Networks Using Neighborhood Information." Information Fusion, vol. 33, pp. 41-56 (2017).
[25] Intel Lab Data (http://db.csail.mit.edu/labdata/labdata.html).
[26] Chatterjee, S.; Mukhopadhyay, A., "Clustering Ensemble: A Multiobjective Genetic Algorithm Based Approach." Procedia Technology, vol. 10, pp. 443-449 (2013).
[27] Jagannath Nanda, S.; Panda, G., "A Survey on Nature Inspired Metaheuristic Algorithms for Partitional Clustering." Swarm and Evolutionary Computation, vol. 16, pp. 1-18 (2014).
[28] Garcia-Font, V.; Garrigues, C.; RifÖPous, H., "A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks." Sensors, vol. 16, no. 6, 868 (2016).
[29] O'Reilly, C.; Gluhak, A.; Imran, M. A.; Rajasegarar, S., "Anomaly Detection in Wireless Sensor Networks in A Non-stationary Environment." IEEE Communications Surveys & Tutorials, vol. 16,no. 3, pp. 1413-1432 (2014).
[30] Rajasegarar, S.; Leckie, C.; Palaniswami, M., "Hyperspherical Cluster-based Distributed Anomaly Detection in Wireless Sensor Networks." Journal of Parallel and Distributed Computing, vol. 74,no. 1, pp. 1833-1847 (2014).
[31] Kumarage, H.; Khalil, I.; Tari, Z.; Zomaya, A., "Distributed Anomaly Detection for Industrial Wireless Sensor Networks Based on Fuzzy Data Modelling." Journal of Parallel and Distributed Computing, vol. 73, no. 6, pp. 790-806 (2013).
[32] Kapoor, A.; Singhal, A., "A comparative study of K-Means, KMeans++ and Fuzzy C-Means clustering algorithms." CICT 2017.3rd International Conference on Computational Intelligence &
Communication Technology. Ghaziabad, India, IEEE, pp. 1-6 (2017).
[33] Erramilli, A.; Roughan, M.; Veitch, D.; Willinger, W., "Self-Similar Traffic and Network Dynamics." Proceedings of the IEEE, vol. 90,no. 5, pp. 800-819 (2002).
[34] Napoletano, P.; Piccoli, F.; Schettini, R., "Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity." Sensors,vol. 18, no. 1, pp. 1-15 (2018).
 
Volume 12, Issue 1
June 2019
Pages 1-13
  • Receive Date: 03 November 2018
  • Revise Date: 25 April 2019
  • Accept Date: 12 May 2019
  • First Publish Date: 01 June 2019