Fuzzy Clustering Algorithm to Identify Sybil Attacks in Vehicular ad Hoc Networks
الموضوعات : Journal of Computer & RoboticsMahdi Maleknasab Ardekani 1 , Mohammad Tabarzad 2 , Mohammad Amin Shayegan 3
1 - Department of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2 - Department of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
3 - Department of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
الکلمات المفتاحية: fuzzy logic, Vanet, Clustering, Sybil attack, directional antenna,
ملخص المقالة :
Abstract: Due to the increasing use of VANET networks and the use of smart systems in these types of networks, their challenges have been the focus of researchers. One of the important challenges of such networks is the security issues that threaten this category of networks. In this article, the Sybil attack, which is one of the security challenges in VANET networks, has been investigated and identified. In a Sybil attack, a node threatens VANET networks by stealing the identity of other nodes or creating a virtual identity, by making incorrect decisions and sending false information. In this article, the clustering method is used to avoid the overhead of identification nodes in centralized methods and avoid delay in distributed methods. RSU determines the cluster head with the help of fuzzy logic. The cluster head creates moving clusters by placing similar nodes in terms of direction, speed, and distance in separate clusters while moving. The cluster head performs malicious node detection using a directional antenna and a fuzzy system. The first fuzzy system places the cluster head in the best possible place of the cluster. The cluster head identifies the malicious nodes in each cluster locally, while the second fuzzy system interferes in determining the validity of the cluster members. In the proposed plan, in addition to optimizing the sending and receiving of messages, The simulation results show that the proposed method has improved by 1.2% in detecting the malicious node, 0.4% in the number of a false positive detection, 0.6% in the lost packet, and 0.1% in the delay compared to the previous methods.
