Sutharshan Rajasegarar
James C. Bezdek
Skip Abstract Section
Abstract
Anomalies in wireless sensor networks can occur due to malicious attacks, faulty sensors, changes in the observed external phenomena, or errors in communication. Defining and detecting these interesting events in energy-constrained situations is an important task in managing these types of networks. A key challenge is how to detect anomalies with few false alarms while preserving the limited energy in the network. In this article, we define different types of anomalies that occur in wireless sensor networks and provide formal models for them. We illustrate the model using statistical parameters on a dataset gathered from a real wireless sensor network deployment at the Intel Berkeley Research Laboratory. Our experiments with a novel distributed anomaly detection algorithm show that it can detect elliptical anomalies with exactly the same accuracy as that of a centralized scheme, while achieving a significant reduction in energy consumption in the network. Finally, we demonstrate that our model compares favorably to four other well-known schemes on four datasets.
- Akyildiz, I., Su, W., Sankarasubramaniam, Y., and Cayirci, E. 2002. Wireless sensor networks: A survey. Comput. Netw. 38, 4, 393--422. Google Scholar
Digital Library
- Barnett, V. and Lewis, T. 1994. Outliers in Statistical Data 3rd Ed. John Wiley and Sons.Google Scholar
- Bettencourt, L. M. A., Hagberg, A. A., and Larkey, L. B. 2007. Separating the wheat from the chaff: Practical anomaly detection schemes in ecological applications of distributed sensor networks. In Proceedings of the 3rd IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS). 223--239. Google ScholarDigital Library
- Bezdek, J. C., Pal, M. R., Keller, J., and Krisnapuram, R. 1999. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Publishers, Norwell, MA. Google ScholarDigital Library
- Bondarenko, O., Kininmonth, S., and Kingsford, M. 2007. Underwater sensor networks, oceanography and plankton assemblages. In Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). 657--662.Google Scholar
- Branch, J., Szymanski, B., Giannella, C., Wolff, R., and Kargupta, H. 2006. In-Network outlier detection in wireless sesnor networks. In Proceedings of the IEEE International Conference on Distributed Computing Systems (ICDCS). Google ScholarDigital Library
- Buonadonna, P., Gay, D., Hellerstein, J., Hong, W., and Madden, S. 2005. Task: Sensor network in a box. In Proceedings of the 2nd European Workshop on Wireless Sensor Networks. 133--144.Google Scholar
- CC2420-Web. 2008. http://focus.ti.com/lit/ds/symlink/cc2420.pdf.Google Scholar
- Chatzigiannakis, V. and Papavassiliou, S. 2007. Diagnosing anomalies and identifying faulty nodes in sensor networks. IEEE Sensors J. 7, 5, 637--645.Google ScholarCross Ref
- Chohan, N. 2007. Hardware assisted compression in wireless sensor networks. www.cs.ucsb.edu/~nchohan/docs/hadcwsnProgressReport.pdf.Google Scholar
- Crossbow-Web. 2008. http://www.xbow.com/Products/productdetails.aspx?sid=156.Google Scholar
- Dave, R. and Krishnapuram, R. 1997. Robust clustering methods: A unified view. IEEE Trans. Fuzzy Syst. 5, 2, 270--293. Google ScholarDigital Library
- Deshpande, A., Guestrin, C., and Madden, S. 2005. Using probabilistic models for data management in acquitional environment. In Proceedings of the 2nd Biennial Conference on Innovative Data Systems Research (CIDR). 317--328.Google Scholar
- Drozda, M., Schaust, S., and Szczerbicka., H. 2007a. Is AIS based misbehavior detection suitable for wireless sensor networks? In Proceedings of the IEEE Wireless Communications and Networking Conference.Google Scholar
- Drozda, M., Schaust, S., and Szczerbicka, H. 2007b. AIS for misbehavior detection in wireless sensor networks: Performance and design principles. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). 3719--3726.Google Scholar
- Du, W., Fang, L., and Peng, N. 2006. LAD: Localization anomaly detection for wireless sensor networks. J. Parallel Distrib. Comput. 66, 7, 874--886. Google ScholarDigital Library
- Duda, R. O., Hart, P. E., and Stork, D. G. 2001. Pattern Classification 2 Ed. John Wiley and Sons Inc. Google ScholarDigital Library
- Frigui, H. and Krishnapuram, R. 1997. Clustering by competitive agglomeration. Pattern Recogn. 30, 7, 1109--1119. Google ScholarDigital Library
- Frigui, H. and Krishnapuram, R. 1999. A robust competitive clustering algorithm with applications in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 21, 5, 450--465. Google ScholarDigital Library
- GBR-Project. 2007. http://www.issnip.unimelb.edu.au/research_program/sensor_networks/core_%technologies/anomaly_detection/anomaly_detection_in_wsn.Google Scholar
- Hodge, V. and Austin, J. 2004. A survey of outlier detection methodologies. In Artificial Intelligence Review. Springer Netherlands, 85--126. Google ScholarDigital Library
- IBRL-Web. 2006. http://db.lcs.mit.edu/labdata/labdata.html.Google Scholar
- IMote2-Web. 2008. http://www.xbow.com/Products/productdetails.aspx?sid=253.Google Scholar
- Johnson, R. A. and Wichern, D. W. 1988. Applied Multivariate Statistical Analysis. Prentice Hall. Google ScholarDigital Library
- Kelly, P. 1994. An algorithm for merging hyperellipsoidal clusters. Tech. rep. LA-UR-94-3306, Los Alamos National Laboratory.Google Scholar
- Kininmonth, S., Bainbridge, S., Atkinson, I., Gill, E., Barral, L., and Vidaud, R. 2004. Sensor networking the great barrier reef. Spatial Sci. QLD J. 34--38.Google Scholar
- Knorr, E. M. and Ng, R. T. 1998. Algorithms for mining distance based outliers in large datasets. In Proceedings of the 24th International Conference on Very Large Data Bases (VLDB). 392--403. Google ScholarDigital Library
- Kotidis, Y., Deligiannakis, A., Stoumpos, V., Vassalos, V., and Delis, A. 2007. Robust management of outliers in sensor nerwork aggregate queries. In Proceedings of the 6th International ACM Workshop on Data Engineering for Wireless and Mobile Access (MobiDE). Google ScholarDigital Library
- Lazos, L. and Poovendran, R. 2005. Serloc: Robust localization for wireless sensor networks. ACM Trans. Sensor Netw. 1, 1, 73--100. Google ScholarDigital Library
- Liu, D., Ning, P., and Du, W. 2005. Detecting malicious beacon nodes for secure location discovery in wireless sensor networks. In Proceedings of the 25th IEEE International Conference on Distributed Computing Systems. 609--619. Google ScholarDigital Library
- Loo, C., Ng, M., Leckie, C., and Palaniswami, M. 2006. Intrusion detection for routing attacks in sensor networks. Int. J. Distrib. Sensor Netw. 2, 4, 313--332.Google ScholarCross Ref
- Markos, M. and Sameer, S. 2003. Novelty detection: A review part 1: Statistical approaches. Signal Proc. 83, 12, 2481--2497. Google ScholarDigital Library
- Mathur, G., Desnoyers, P., Ganesan, D., and Shenoy, P. 2006. Ultra low power data storage for sensor networks. In Proceedings of the 5th Intlrnational Conference on Information Processing in Sensor Networks. ACM Press, New York, 374--381. Google ScholarDigital Library
- Paladugu, K., Santhanam, L., Xie, B., and Agrawal, D. P. 2007. Dclad: Distributed cluster based localization anomaly detection in wireless sensor networks using single mobile beacon. In Proceedings of the IEEE Military Communications Conference (MILCOM). 1--7.Google Scholar
- Papadimitriou, S., Kitagawa, H., Gibbons, P., and Faloutsos, C. 2003. LOCI: Fast outlier detection using the local correlation integral. In Proceedings of 19th International Conference on Data Engineering. 315--326.Google Scholar
- Perrig, A., Stankovic, J., and Wagner, D. 2004. Security in wireless sensor networks. Commun. ACM 47, 6, 53--57. Google ScholarDigital Library
- Petrovskiy, M. I. 2003. Outlier detection algorithms in data mining systems. Program. Comput. Softw. 29, 4, 228--237. Google ScholarDigital Library
- Pottie, G. J. and Kaiser, W. J. 2000. Wireless integrated network sensors. Comm. ACM 43, 5, 51--58. Google ScholarDigital Library
- Raghunathan, V., Schurgers, C., Park, S., and Srivastava, M.B. 2002. Energyaware wireless microsensor networks. IEEE Signal Proces. Mag.Google ScholarCross Ref
- Rajasegarar, S., Gubbi, J., Bondarenko, O., Kininmonth, S., Marusic, S., Bainbridge, S., Atkinson, I., and Palaniswami, M. 2008a. Sensor network implementation challenges in the great barrier reef marine environment. In Proceedings of the SENSEI-ISSNIP Workshop alongside ICT Mobile Summit.Google Scholar
- Rajasegarar, S., Leckie, C., and Palaniswami, M. 2008b. Anomaly detection in wireless sensor networks. IEEE Wirel. Commun. Mag. 15, 4, 34--40. Google ScholarDigital Library
- Rajasegarar, S., Leckie, C., and Palaniswami, M. 2008c. CESVM: Centered hyperellipsoidal support vector machine based anomaly detection. In Proceedings of the IEEE International Conference on Communications (ICC). 1610--1614.Google Scholar
- Rajasegarar, S., Leckie, C., Palaniswami, M., and Bezdek, J. 2006. Distributed anomaly detection in wireless sensor networks. In Proceedings of the IEEE International Conference on Communications Systems (ICCS).Google Scholar
- Rajasegarar, S., Leckie, C., Palaniswami, M., and Bezdek, J. C. 2007. Quarter sphere based distributed anomaly detection in wireless sensor networks. In Proceedings of the IEEE International Conference on Communications (ICC). 3864--3869.Google Scholar
- Srinivasan, A. and Wu, J. 2008. A survey on secure localization in wireless sensor networks. Encyclopedia of Wireless and Mobile Communications. To appear. http://www.cse.fau.edu/~jie/research/publications/Publication_files/BookChapter_SurveyOnSecureLocalizationInWirelessSensorNetworks.pdf.Google Scholar
- Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., and Gunopulos, D. 2006. Online outlier detection in sensor data using non-parametric models. In Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB). 187--198. Google ScholarDigital Library
- Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., and Estrin, D. 2004. Habitat monitoring with sensor networks. Commun. ACM. 47, 34--40. Google ScholarDigital Library
- TinyOS-Web. 2008. http://www.tinyos.net/tinyos-2.x/doc/html/tep126.html.Google Scholar
- Wark, T., Crossman, C., Hu, W., Guo, Y., Valencia, P., Sikka, P., Corke, P., Lee, C., Henshall, J., Prayaga, K., O'Grady, J., Reed, M., and Fisher, A. 2007. The design and evaluation of a mobile sensor/actuator network for autonomous animal control. In Proceedings of the 6th International Conference on Information Processing in Sensor Networks (IPSN). ACM Press, New York, 206--215. Google ScholarDigital Library
- Younis, O. and Fahmy, S. 2004. Distributed clustering in ad-hoc sensor networks: A hybrid, energy-efficient approach. In Proceedings of IEEE InfoCom. Vol. 1. 640.Google Scholar
- Zhao, F., Liu, J., Liu, J., Guibas, L., and Reich, J. 2003. Collaborative signal and information processing: An information-directed approach. Proc. IEEE 91, 8, 1199--1209.Google ScholarCross Ref
Index Terms
- Elliptical anomalies in wireless sensor networks
Recommendations
Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks
Anomaly detection in wireless sensor networks is an important challenge for tasks such as intrusion detection and monitoring applications. This paper proposes two approaches to detecting anomalies from measurements from sensor networks. The first ...
Outlier Detection Approach Using Bayes Classifiers in Wireless Sensor Networks
Wireless sensor networks (WSN) have become a new information collection and monitoring solution for a variety of applications. Sensor nodes may occasionally produce incorrect measurements due to battery depletion, damage of device and other causes. ...
An adaptive elliptical anomaly detection model for wireless sensor networks
Wireless Sensor Networks (WSNs) provide a low cost option for monitoring different environments such as farms, forests and water and electricity networks. However, the restricted energy resources of the network impede the collection of raw monitoring ...
Comments