Intrusion detection in Gaussian distributed Wireless Sensor Networks

Intrusion detection in a Wireless Sensor Network (WSN) is of significant importance in many applications to detect malicious or unexpected intruder(s). The intruder can be an enemy in a battlefield, or a unusual environmental change in a chemical industry etc. With uniform distribution, the detection probability is the same for any point in a WSN. However, some applications may require different degrees of detection probability at different locations in the deployment area. Gaussian distributed WSNs (i.e., normal distribution) can provide differentiated detection capabilities at different locations and are widely deployed in practice. In view of this, this paper analyzes the problem of intrusion detection in a Gaussian distributed WSN, by characterizing intrusion detection probability with respect to intrusion distance and network deployment parameters. Two detection models are considered: single-sensing detection and multiple-sensing detection. Effects of different network parameters on the intrusion detection probability are examined in details. This work allows us to analytically formulate the intrusion detection probability within a certain intrusion distance under various application scenarios, therefore provides insight for directing the application-specific WSN deployment such as intrusion detection.

[1]  S. Leigh,et al.  Probability and Random Processes for Electrical Engineering , 1989 .

[2]  Patrick Thiran,et al.  Delay of intrusion detection in wireless sensor networks , 2006, MobiHoc '06.

[3]  Younghwan Yoo,et al.  Impact of Node Density and Sensing Range on Intrusion Detection in Wireless Sensor Networks , 2006, Proceedings of 15th International Conference on Computer Communications and Networks.

[4]  Nael B. Abu-Ghazaleh,et al.  A taxonomy of wireless micro-sensor network models , 2002, MOCO.

[5]  Donald F. Towsley,et al.  Mobility improves coverage of sensor networks , 2005, MobiHoc '05.

[6]  Srdjan Capkun,et al.  GPS-free Positioning in Mobile Ad Hoc Networks , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[7]  Dharma P. Agrawal,et al.  Intrusion Detection in Homogeneous and Heterogeneous Wireless Sensor Networks , 2008, IEEE Transactions on Mobile Computing.

[8]  S. Banerjee,et al.  Intrusion Detection on Sensor Networks Using Emotional Ants , 2005 .

[9]  Dharma P. Agrawal,et al.  Introduction to Wireless and Mobile Systems , 2002 .

[10]  Dharma P. Agrawal,et al.  Localization Algorithm using Expected Hop Progress in Wireless Sensor Networks , 2006, 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Systems.

[11]  Dragos Niculescu,et al.  Positioning in ad hoc sensor networks , 2004, IEEE Network.

[12]  Xin Chen,et al.  Design and Analysis of Sensing Scheduling Algorithms under Partial Coverage for Object Detection in Sensor Networks , 2007, IEEE Transactions on Parallel and Distributed Systems.

[13]  Yu-Chee Tseng,et al.  Efficient in-network moving object tracking in wireless sensor networks , 2006, IEEE Transactions on Mobile Computing.

[14]  Deborah Estrin,et al.  GPS-less low-cost outdoor localization for very small devices , 2000, IEEE Wirel. Commun..

[15]  Dharma P. Agrawal,et al.  Coverage and Lifetime Optimization of Wireless Sensor Networks with Gaussian Distribution , 2008, IEEE Transactions on Mobile Computing.

[16]  H. T. Kung,et al.  Efficient location tracking using sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[17]  JAMAL N. AL-KARAKI,et al.  Routing techniques in wireless sensor networks: a survey , 2004, IEEE Wireless Communications.