# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 1 Introduction  
Vehicular ad hoc networks (VANETs) play a vital role in the growth and the use of self-driving and semi self-driving vehicles [1]. Internal and external communication systems are considered important components in autonomous and semi-autonomous cars. VANETs represent the communication between vehicles (V2R) and their Road Side Units (RSUs) or intra vehicular communication (V2V) in radio coverage areas, as shown in Figure 1.  
External communication between self-driving vehicles and roadside equipment in Intelligent Transportation System (ITS) depend primarily on IEEE 802.11p wireless transmission [2]. In autonomous and semi-autonomous vehicles, the communication utilises Cooperative Awareness Messages (CAMs) or Basic Safety Message (BSM), which are transferred between RSUs and vehicles or just between vehicles in that zone [3]. The major objective of ITS communication is to enable traffic and passengers' safety. VANETs [4] are mobile nodes that facilitate communication in a particular zone as well as with RSUs in the absence of a fixed security infrastructure, which is used in conventional networks like wired networks [5]. Many researches consider VANETs a subclass or subtype of Mobile Ad hoc Networks (MANETs) [3]. They directly affect the ITS through the provision of comfortservices and safety applications to drivers and passengers. The major goal of VANETs is to guarantee safety of road users and also the vehicles themselves. As indicated above, through the exchange of warning message and control data, these networks can achieve their goals and provide emergency and comfort notifications to passengers and drivers, such as messages concerning emergency braking or accidents [6]. In ad hoc networks, three types of routing approaches are utilised: (1) the proactive approach; (2) the reactive approach; and (3) the hybrid approach [7]. One of the reactive protocols that is generally applied in external communication for autonomous vehicles is the routing protocol utilising the demand vector. High rate of throughput, low rate of delay and sequence numbering are key factors for the selection of the Ad hoc On-Demand Distance Vector (AODV) routing protocol [7]. The usage of sequence numbers enables AODV to perform more proficiently when compared with other routing protocols. Authentication of messages and vehicles in cooperative vehicular ad hoc networks safety, warning messages, control data, and notification messages is needed in self-driving and semi self-driving vehicles. Traditional security systems like encryption/decryption methods may be able to prevent external attacks from achieving their goal of hacking sensitive information and data control between RSUs and self-driving vehicles. However, these vehicles do not possess the ability to secure external communication from internal attacks. In addition, every layer of the VANETs is vulnerable to attacks [8; 9], and this makes its security one of the greatest challenges [9]. This paper focuses on protecting the external communication system from internal/external attacks on the network layer, such as Denial of Service (DoS), black hole, grey hole, wormhole and rushing attacks [10]. Grey hole and rushing attacks can stop cooperation, which results in disconnection between the vehicles and the road side units (RSUs) [10]. Creation of a suitable security system for the routing protocols is required, as the network layers work directly with routing protocols. Instead of forwarding packets to the correct destination node, malicious nodes displace or drop packets. For such attacks, most received packets are dropped and are not moved to their destination node. Other problems can arise in such an attack, for instance an overhead increase and Packet Delivery Rate (PDR) decrease on the network [10]. The attack detection is a difficult task owing to selective dropping of data packets [11].