Speed Adjustment Attack on Cooperative Sensing in Cognitive Vehicular Networks

Abstract

The ever-increasing demand for vehicular traffic consumption in 5G makes the problem of spectrum scarcity in vehicular networks more serious. In order to solve this problem, cognitive radio (CR) technology has been used in vehicular networks, leading to cognitive vehicular networks (CVNs). However, different from traditional cognitive networks, the high mobility of CVNs makes cooperative spectrum sensing more challenging, and new attacks are frequently emerging. In this paper, we address a speed adjustment attack (SAA) on cooperative sensing in CVNs. In this attack, attackers can affect the spectrum sensing data of their neighbors by dynamically adjusting moving speed. Therefore, this attack can speed up the spread of error-sensing data across the entire network with changing neighbors. The simulation results show that the SAA can mislead the sensing result more quickly without detection. With Historical Data Information detection, it can significantly slow down the convergence time, potentially resulting in algorithmic divergence.