Delimitated Anti Jammer Scheme for Internet of Vehicle: Machine Learning Based Security Approach

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.