Title: Evaluating multidimensional RSSI-based approaches for identifying Sybil nodes in VANETs to improve vehicle network security

Authors: G. Shobana; Arockia Xavier Annie Rayan

Addresses: Department of Information Technology, Loyola-ICAM College of Engineering and Technology, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, CEG, Anna University, Chennai, Tamil Nadu, India

Abstract: Sybil attacks pose a significant security risk to ad hoc sensor networks, distorting information on traffic congestion, fake message transfers, etc. To address this issue, a novel technique for detecting Sybil attacks in VANETs makes use of node positions, distances, and received signal strength indicator (RSSI) values, whereas the legitimate nodes exhibit variations due to their various physical locations. The key idea is to identify Sybil nodes based on their similar RSSI and use a machine-learning model to detect Sybil nodes with high accuracy. In this work, the RSSI-based Sybil node identification (RSSI-SNID) technique, intended for Sybil node identification in a VANET environment, is used to construct a Sybil attack model. By integrating RSSI, distance, and timestamps in a multidimensional analysis, RSSI-SNIDs improve VANET's overall functionality in identifying Sybil attacks and attain 98% accuracy with a low false-positive rate, making it unique in its ability to identify these nodes.

Keywords: vehicular ad hoc network; VANET; Sybil attack identification; received signal strength indicator; RSSI; machine learning; multidimensional analysis.

DOI: 10.1504/IJAHUC.2024.140667

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.47 No.1, pp.45 - 58

Received: 17 Jan 2024
Accepted: 30 May 2024
Published online: 29 Aug 2024 *

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