Analysis of Detection of Multiple Attackers in Wireless Networks

Wireless networks are usually deployed in hostile environment where an adversary can masquerade some internal nodes which may launch various inside attacks which may leads to reduction in network performance. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper one method is used based on spatial correlation of Received signal strength of each node to find out the presence of attack and a cluster based mechanism is used to find out number of attackers. An efficiency based RADAR gridded algorithm is used further to localize the no of attackers in the network. Experimental evaluation is carried out using two test simulations of IEEE 802.11 and Zigbee networks. The comparison shows that the packet overheads are lesser as compared to other schemes. It has been observed that packet delivery ratio and end to end delay increases as increase number of nodes while energy decreases with optimal point.

[1]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[2]  Wade Trappe,et al.  An authentication framework for hierarchical ad hoc sensor networks , 2003, WiSe '03.

[3]  Richard P. Martin,et al.  Empirical Evaluation of the Limits on Localization Using Signal Strength , 2009, 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[4]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[5]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[6]  Santosh Pandey,et al.  A survey on localization techniques for wireless networks , 2006 .

[7]  Jon Crowcroft,et al.  Delivery Properties of Human Social Networks , 2009, IEEE INFOCOM 2009.

[8]  Jie Yang,et al.  Determining the Number of Attackers and Localizing Multiple Adversaries in Wireless Spoofing Attacks , 2009, IEEE INFOCOM 2009.

[9]  David R. Cheriton,et al.  Detecting identity-based attacks in wireless networks using signalprints , 2006, WiSe '06.

[10]  Jie Wu,et al.  Secure and efficient key management in mobile ad hoc networks , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[11]  Jie Zheng,et al.  Estimating the Number of Clusters via System Evolution for Cluster Analysis of Gene Expression Data , 2009, IEEE Transactions on Information Technology in Biomedicine.

[12]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[13]  Richard P. Martin,et al.  A Practical Approach to Landmark Deployment for Indoor Localization , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[14]  Tzi-cker Chiueh,et al.  Sequence Number-Based MAC Address Spoof Detection , 2005, RAID.

[15]  Yong Sheng,et al.  Detecting 802.11 MAC Layer Spoofing Using Received Signal Strength , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[16]  Silvia Giordano,et al.  Mobile ad hoc networks , 2002 .

[17]  Douglas C. Madory,et al.  New Methods of Spoof Detection in 802.11b Wireless Networking , 2006 .

[18]  Jie Wu,et al.  Secure and Efficient Key Management in Mobile Ad Hoc Networks , 2005, IPDPS.