Detecting Sybil attacks in Wireless Sensor Networks using neighboring information

As the prevalence of Wireless Sensor Networks (WSNs) grows in the military and civil domains, the need for network security has become a critical concern. In a Sybil attack, the WSN is subverted by a malicious node which forges a large number of fake identities in order to disrupt the network's protocols. In attempting to protect WSNs against such an attack, this paper develops a scheme in which the node identities are verified simply by analyzing the neighboring node information of each node. The analytical results confirm the efficacy of the approach given a sufficient node density within the network. The simulation results demonstrate that for a network in which each node has an average of 9 neighbors, the scheme detects 99% of the Sybil nodes with no more than a 4% false detection rate. The experiment result shows that the Sybil nodes can still be identified when the links are not symmetric.

[1]  Kuo-Feng Ssu,et al.  Using overhearing technique to detect malicious packet-modifying attacks in wireless sensor networks , 2007, Comput. Commun..

[2]  李辉,et al.  TDOA-based Sybil attack detection scheme for wireless sensor networks , 2008 .

[3]  A. Perrig,et al.  The Sybil attack in sensor networks: analysis & defenses , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[4]  Lisa Ann Osadciw,et al.  Prediction of Sybil attack on WSN using Bayesian network and swarm intelligence , 2008, SPIE Defense + Commercial Sensing.

[5]  Peng Ning,et al.  Defending against Sybil attacks in sensor networks , 2005, 25th IEEE International Conference on Distributed Computing Systems Workshops.

[6]  Elaine Shi,et al.  The Sybil attack in sensor networks: analysis & defenses , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[7]  Fang Liu,et al.  Real-Time Detection of Clone Attacks in Wireless Sensor Networks , 2008, 2008 The 28th International Conference on Distributed Computing Systems.

[8]  Geng Yang,et al.  Sybil Attack Detection Based on RSSI for Wireless Sensor Network , 2007, 2007 International Conference on Wireless Communications, Networking and Mobile Computing.

[9]  Chuang Lin,et al.  Security mechanisms Analysis of Wireless Sensor Networks specific Routing attacks , 2006, 2006 First International Symposium on Pervasive Computing and Applications.

[10]  Murat Demirbas,et al.  An RSSI-based scheme for sybil attack detection in wireless sensor networks , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).

[11]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[12]  Donggang Liu,et al.  Establishing pairwise keys in distributed sensor networks , 2005, TSEC.

[13]  Jiannong Cao,et al.  Maximizing network lifetime based on transmission range adjustment in wireless sensor networks , 2009, Comput. Commun..

[14]  Hewijin Christine Jiau,et al.  Detection and diagnosis of data inconsistency failures in wireless sensor networks , 2006, Comput. Networks.

[15]  R. Farivar,et al.  Directed flooding: a fault-tolerant routing protocol for wireless sensor networks , 2005, 2005 Systems Communications (ICW'05, ICHSN'05, ICMCS'05, SENET'05).

[16]  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..

[17]  Joseph M. Hellerstein,et al.  Clock Skew Based Node Identification in Wireless Sensor Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[18]  John R. Douceur,et al.  The Sybil Attack , 2002, IPTPS.

[19]  Yuguang Fang,et al.  Location-based compromise-tolerant security mechanisms for wireless sensor networks , 2006, IEEE Journal on Selected Areas in Communications.