Recently, Internet of vehicles (IoV) has witnessed significant research and development attention in both academia and industries due to the potential towards addressing traffic incidences and supporting green mobility. With the growing vehicular network density, jamming signal centric security issues have become challenging task for IoV network designers and traffic applications developers. Global positioning system (GPS) and roadside unit (RSU) centric related literature on location-based security approaches lacks signal characteristics consideration for identifying vehicular network intruders or jammers. In this context, this paper proposes a machine learning oriented as Delimitated Anti Jamming protocol for vehicular traffic environments. It focuses on jamming vehicle's discriminated signal detection and filtration for revealing precise location of jamming effected vehicles. In particular, a vehicular jamming system model is presented focusing on localization of vehicles in delimitated jamming environments. A foster rationalizer is employed to examine the frequency changes caused in signal strength due to the jamming or external attacks. A machine learning open-sourced algorithm namely, CatBoost has been utilized focusing on decision tree relied algorithm to predict the locations of jamming vehicle. The performance of the proposed anti jammer scheme is comparatively evaluated with the state of the art techniques. The evaluation attests the resistive characteristics of the anti-jammer technique considering precision, recall, F1 score and delivery accuracy metrics. INDEX TERMS Internet of Vehicles, location verification, jamming signal, machine learning. I. INTRODUCTION Vehicular networks are emerging as a new promising field of wireless technology, where security is one of the major research theme [1]. A cooperative group of sensor-enabled vehicles operating in a dynamic road traffic network environment by interconnecting among on-road vehicles and, with neighboring Road Side Units (RSUs) are referred to as Vehicular Ad-hoc Networks (VANETs) [2]. The three sorts of data transmission to disseminate cooperative messages includes Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) The associate editor coordinating the review of this article and approving it for publication was Guan Gui. and Infrastructure-to-Vehicle (I2V) (see Fig. 1). The sensor enabled vehicles can communicate with each other in V2V data transmission either through direct wireless range or indirect multi-hop mode of communication [3]. V2I represents the communication among vehicles and the roadside infrastructures for avoiding vehicular incidences and enabling road safety. VANETs can support a promising intelligent transportation system technology for many real time traffic applications including safety message dissemination, dynamic route planning, content distribution, gaming, and Internet of connected vehicles, connected autonomous vehicles, electric vehicles and related smart applications [4]. This real time traffic information orient...
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