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Privacy friendly detection technique of sybil attack in vehicular ad hoc network (VANET)

Cheraghi, Seyed Mohammad (2013) Privacy friendly detection technique of sybil attack in vehicular ad hoc network (VANET). Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System.

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Abstract

Vehicular communications play a substantial role in providing safety in transportation by means of safety message exchange. Researchers have proposed several solutions for securing safety messages. Protocols based on a fixed key infrastructure are more efficient in implementation and maintain stronger security in comparison with dynamic structures. Security is an important concern in VANETs because a malicious user may deliberately mislead other vehicles and vehicular agencies. Sybil attacks have been regarded as a serious security threat to ad hoc networks and sensor networks. They may also impair the potential applications of VANETs (Vehicular Ad hoc Networks) by creating an illusion of traffic congestion. Sybil attack is a malicious vehicle pretends to be multiple vehicles. Reported data from a Sybil attacker will appear to arrive from a large number of distinct vehicles, and hence will be credible. Privacy is another issue that must be preserved. In order to maintain security, privacy ca be compromised, hence this research study proposes a privacy friendly framework to detect Sybil attack. Finally, we use MATLAB as a simulator and compare the simulation results with other method.

Item Type:Thesis (Masters)
Additional Information:Thesis (Sarjana Sains Komputer (Keselamatan Maklumat)) - Universiti Teknologi Malaysia, 2013; Supervisor : Dr. Mohammad Abdur Razaque
Uncontrolled Keywords:ad hoc networks (computer networks), wireless communication systems
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Computer Science and Information System (Formerly known)
ID Code:34666
Deposited By: Kamariah Mohamed Jong
Deposited On:19 Feb 2014 10:24
Last Modified:11 Sep 2017 11:01

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