Efficient Detection and Localization of DoS Attacks in Heterogeneous Vehicular Networks

Vehicular communication has emerged as a powerful tool for providing a safe and comfortable driving experience for users. Long Term Evolution (LTE) supports and enhances the quality of vehicular communication due to its properties such as high data rate, spatial reuse, and low delay. However, high mobility of vehicles introduces a wide variety of security threats, including Denial-of-Service (DoS) attacks. In this paper, we propose effective solutions for real-time detection and localization of DoS attacks in an LTE-based vehicular network with mobile network components (e.g., vehicles, femto access points, etc.). We consider malicious data transmission by vehicles in two ways- using real identification (unintentional) and using fake identification (intentional). This paper makes three important contributions. First, we propose an efficient attack detection technique based on data packet counter and average Packet Delivery Ratio (PDR). Next, we present an improved attack detection framework using machine learning algorithms. We use some ML-based supervised classification algorithms to make detection more robust and consistent. Finally, we propose Data Packet Counter (DPC)-based, triangulation-based and measurement report based localization for both intentional and unintentional DoS attacks. We analyze the average packet delay incurred by vehicles by modelling the system as an M/M/m queue. Our experimental evaluation demonstrates that our proposed technique significantly outperforms state-of-the-art techniques.