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Fuzzy based Sybil attack detection in Wireless Sensor Network
Open Access   Article

Fuzzy based Sybil attack detection in Wireless Sensor Network
Palak 1
1 Dept. Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India .
Correspondence should be addressed to:

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-11 , Page no. 50-56, Nov-2017


Online published on Nov 30, 2017

Copyright © Palak . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Palak, “Fuzzy based Sybil attack detection in Wireless Sensor Network”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.50-56, 2017.

MLA Style Citation: Palak "Fuzzy based Sybil attack detection in Wireless Sensor Network." International Journal of Computer Sciences and Engineering 5.11 (2017): 50-56.

APA Style Citation: Palak, (2017). Fuzzy based Sybil attack detection in Wireless Sensor Network. International Journal of Computer Sciences and Engineering, 5(11), 50-56.
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Abstract :
Wireless Sensor Networks (WSNs) are mostly vulnerable to the various attacks. The performance of the wireless sensor networks plays vital role but attacks degrades the performance. One of the attacks is the Sybil attack, in which a malicious node creates a huge number of fake identities in the network. The study indicates that the UWB ranging-based Sybil attack detection in wireless sensor network has better results but it can be improved further by utilizing the optimistic decision making technique. This research work mainly focus on the wireless sensor network which use fuzzy membership function for Sybil attack detection which further improves the WSNs. This proposed technique provides 95% accuracy and higher the value of F-measure and lower the false probability rate and error rate.
Key-Words / Index Term :
Wireless Sensor Network, Sybil attack, Fuzzy membership function
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