Arpita M. Bhise

Work place: Yeshawantrao Chavan College of Engineering/Department of Computer Science and Engineering, Nagpur, 441110, India

E-mail: arpita.bhise08@gmail.com

Website:

Research Interests: Information Security, Network Security, Information Systems, Information Storage Systems, Multimedia Information System, Algorithmic Information Theory

Biography

Arpita M. Bhise is a PG Scholar at Yeshwantarao Chavan College of Engineering, Nagpur (An Autonomous Institution Affiliated to Rashtrasant Tukdoji Maharaj Nagpur University). She received Bachelor’s degree (B.E.) in Information Technology, from St. Vincent Pallotti College of Engineering, Nagpur. She also has received Master’s degree (Master of Business Administration) in Human Resources subject from Pune University. Her main research area includes network security, communication security and information security.

Author Articles
Detection and Mitigation of Sybil Attack in Peer-to-peer Network

By Arpita M. Bhise Shailesh D. Kamble

DOI: https://doi.org/10.5815/ijcnis.2016.09.08, Pub. Date: 8 Sep. 2016

Peer-to-peer networks are widely used today. Due to this wide use, they are the target of many attackers. The most mentionable of them is the Sybil attack. This is an attack in which it creates many fake identities. In this paper, the detection scheme and efficient mitigation mechanism to counteract Sybil attack in the peer-to-peer network is proposed. The proposed Sybil detection scheme is used to detect Sybil attack. The detection of Sybil attack is depending upon the behavior of the packets. The identity and the location of the packet are checked. If the location and identity of the packet are changed than that of the mentioned, the packet is detected as a Sybil attack. Sybil mitigation scheme is the combination of cost incurred method and certified authentication method. The Sybil packet will be removed by closing read/write operations. The proposed scheme is evaluated on the basis of detection rate and false positive rate. The experimental results show that Sybil attack is accurately detected by the proposed system in terms of low false positive rate and high detection rate. Moreover, the proposed system works efficiently in terms of Sybil detection rate and false positive rate.

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