Differential Kullback-Leibler Divergence Based Anomaly Detection Scheme in Sensor Networks

The constrained capacity of wireless sensor nodes and harsh, unattended deploy environments make the data collected by sensor nodes usually unreliable. In this paper, we propose a Differential Kullback-Leibler Divergence based anomaly detection scheme with the goal of detecting anomaly data values. It first partitions the whole sensor network into several clusters in which the sensors in each cluster are physically close to each other and have the similar sensed values. Then, the cluster header detects the outliers within the current cluster using Kullback-Leibler Divergence in a differential manner. The proposed scheme is lightweight and energy efficient than the existing detection scheme while maintaining the similar detection performance in terms of detection accuracy ratio and false alarm ratio.

[1]  C.-C. Jay Kuo,et al.  Distributed spatio-temporal outlier detection in sensor networks , 2005, SPIE Defense + Commercial Sensing.

[2]  Symeon Papavassiliou,et al.  Hierarchical Anomaly Detection in Distributed Large-Scale Sensor Networks , 2006, 11th IEEE Symposium on Computers and Communications (ISCC'06).

[3]  Dimitrios Gunopulos,et al.  Distributed deviation detection in sensor networks , 2003, SGMD.

[4]  Nirvana Meratnia,et al.  Outlier Detection Techniques for Wireless Sensor Networks: A Survey , 2008, IEEE Communications Surveys & Tutorials.

[5]  Jianfeng Zhang.  APPLICATIONS OF A ROBUST DISPERSION ESTIMATOR , 2011 .

[6]  Jianzhong Li,et al.  Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree , 2007, ADMA.

[7]  Lei Chen,et al.  In-network Outlier Cleaning for Data Collection in Sensor Networks , 2006, CleanDB.

[8]  Moustafa Ghanem,et al.  Distributed Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks , 2011, IEEE Sensors Journal.

[9]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[10]  Bo Sheng,et al.  Outlier detection in sensor networks , 2007, MobiHoc '07.

[11]  Maria E. Orlowska,et al.  On the Optimal Robot Routing Problem in Wireless Sensor Networks , 2007 .

[12]  Dimitrios Gunopulos,et al.  Online outlier detection in sensor data using non-parametric models , 2006, VLDB.

[13]  S. Sinanovic,et al.  Hardware implementation of a Kullback-Leibler Divergence based signal anomaly detector , 2009, 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies.

[14]  Xiuzhen Cheng,et al.  Localized Outlying and Boundary Data Detection in Sensor Networks , 2007 .

[15]  Jingsha He,et al.  Group-based intrusion detection system in wireless sensor networks , 2008, Comput. Commun..

[16]  D. Janakiram,et al.  Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks , 2006, 2006 1st International Conference on Communication Systems Software & Middleware.

[17]  B. R. Badrinath,et al.  Context-Aware Sensors , 2004, EWSN.

[18]  Marimuthu Palaniswami,et al.  Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks , 2007, 2007 IEEE International Conference on Communications.

[19]  M. Palaniswami,et al.  Distributed Anomaly Detection in Wireless Sensor Networks , 2006, 2006 10th IEEE Singapore International Conference on Communication Systems.

[20]  C. Karlof,et al.  Secure routing in wireless sensor networks: attacks and countermeasures , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..