An efficient DDoS attack detection framework for vehicular communication / Raenu Kolandaisamy - Raenu , Kolandaisamy (2020)
Vehicular Ad Hoc Networks (VANETs) are rapidly gaining attention due to the diversity of services that they can potentially offer. VANET is a wireless network that allows vehicles to interconnect and communicate with other nearby vehicles and Road Side Units (RSUs). In VANET, each vehicle is considered as a node which is equipped with an On-Board Unit (OBU) and an Application Unit (AU). The nodes may connect and communicate with each other directly (i.e., Vehicle to Vehicle (V2V)) or through RSUs (i.e., Vehicle to Infrastructure (V2I)). This is primarily for alleviating an Intelligent Transport System (ITS) that aims to provide a wide range of applications and services including safety, non-safety, and infotainment. The aim of this research is to enhance the detection of DDoS attacks on vehicular communication in VANET environments. This enhanced detection of DDoS attack will provide secure and safe vehicular environment for the drivers and passengers to access the VANET applications and services without having to face disturbance or unavailability of services. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS) attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from higher detection time. To overcome these problems, we present an efficient DDoS attack detection framework which consists of important techniques, i.e. MVSA and SPPA. During V2V communication, DDoS attacks may occur without the users/drivers realizing or being fully aware about it. In the MVSA model we consider small scale vehicular environments. The MVSA model maintains the multiple stages for the detection of DDoS attacks in vehicular networks. The model observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. In the SPPA model, a cluster-based attack detection in data collection was considered, where the leaf nodes pass the sensitive information to the cluster head. The existence of malicious nodes threatens decision making by sending malicious information and sometimes sending many packets to the vehicle node. To overcome this issue, a Stream Position Performance Analysis (SPPA) model has been proposed. This model is used for big scale vehicle environments. This approach monitors the position of any field station in sending the information to perform a Distributed Denial of Service (DDoS) attack. The performance of the MVSA and SPPA methods is evaluated using a Ns2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA and SPPA regarding attack detection time and reducing the impact on vehicular communication on VANET.