MALICIOUS NODE DETECTION USING A DUAL THRESHOLD IN WIRELESS SENSOR NETWORKS

Sensor networks for various event detection applications cannot function effectively if they are vulnerable to attacks. Malicious nodes can generate incorrect readings and misleading reports in such a way that event detection accuracy and false alarm rates are unacceptably low and high, respectively. In our work we present a malicious node detection scheme for wireless sensor networks. Unlike others using a single threshold, the proposed scheme employs two thresholds to cope with the strong trade-off between event detection accuracy and false alarm rate, resulting in improved malicious node detection performance. In addition, each sensor node maintains the trust values of its neighboring nodes to reflect their behavior in decision-making. We use Leach Protocol for detecting the intruder (malicious) node in network with the help of secure routing approaches. Our simulation results show the comparison between the nodes with false alarm rate and Packet delivery ratio with LEACH protocol.

[1]  Muhammad Zahid Khan,et al.  FAULT MANAGEMENT IN WIRELESS SENSOR NETWORKS , 2013 .

[2]  Wan Jian,et al.  Tree Topology Based Fault Diagnosis in Wireless Sensor Networks , 2009, 2009 International Conference on Wireless Networks and Information Systems.

[3]  Subhash Challa,et al.  Survey of trust models in different network domains , 2010, ArXiv.

[4]  Zhou Su,et al.  Malicious node detection in wireless sensor networks using weighted trust evaluation , 2008, SpringSim '08.

[5]  Xiuzhen Cheng,et al.  Localized fault-tolerant event boundary detection in sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[6]  Chiu-Kuo Liang,et al.  A Fault-Tolerant Event Boundary Detection Algorithm in Sensor Networks , 2007, ICOIN.

[7]  Yoon-Hwa Choi,et al.  Fault detection of wireless sensor networks , 2008, Comput. Commun..

[8]  Daniel Curiac,et al.  Malicious Node Detection in Wireless Sensor Networks Using an Autoregression Technique , 2007, International Conference on Networking and Services (ICNS '07).

[9]  Ping Li,et al.  Distributed weighting fault-tolerant algorithm for event region detection in wireless sensor networks , 2008, 2008 International Conference on Communications, Circuits and Systems.

[10]  Zhou Su,et al.  Weighted trust evaluation-based malicious node detection for wireless sensor networks , 2009, Int. J. Inf. Comput. Secur..

[11]  Wang-Chien Lee,et al.  Using sensorranks for in-network detection of faulty readings in wireless sensor networks , 2007, MobiDE '07.

[12]  Yaqiong Liu,et al.  An Improved Intrusion Detection Scheme Based on Weighted Trust Evaluation for Wireless Sensor Networks , 2010, 2010 Proceedings of the 5th International Conference on Ubiquitous Information Technologies and Applications.

[13]  Ming Dong,et al.  On distributed fault-tolerant detection in wireless sensor networks , 2006, IEEE Transactions on Computers.

[14]  Miao Xie,et al.  Anomaly Detection in Wireless Sensor Networks , 2013 .

[15]  Ayman I. Kayssi,et al.  Malicious Node Detection in Wireless Sensor Networks , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[16]  Baohua Zhao,et al.  Data Discrimination in Fault-Prone Sensor Networks , 2010, Wirel. Sens. Netw..

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

[18]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[19]  S. Sitharama Iyengar,et al.  Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks , 2004, IEEE Transactions on Computers.

[20]  S. Challa,et al.  Can we trust trusted nodes in wireless sensor networks? , 2008, 2008 International Conference on Computer and Communication Engineering.