# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 2 Related Works  
Traffic accidents are a major cause of death and serious injuries [13]. In the field of self-driving cars, VANETs are developed to enhance the safety of passengers/drivers and vehicles. Their aim is to ensure the safety of the users on the road through enhanced traffic systems that achieve a reduction in the number of accidents arising from human errors. Self-driving vehicles require access to essential information like signs/warning messages and CAMs that are exchanged between vehicles and RSUs in real-time. The performance of VANETs is improved by strengthening their defences against malicious attacks. [PERSON] et al. have enhanced detection rate and reduced the number of false alarms that was generated by IDS [14] based on a Defense Advanced Research Projects Agency (DARPA) dataset utilising a hybrid IDS to secure host/network from the potential attacks. In [15], statistical techniques are used in building intrusion detection systems for vehicular ad hoc networks to identify rogue nodes. The authors can improve the IDS application layer that was based on CAMs for efficient detection malicious of behaviour for high change dynamic. [PERSON] proposes a security system to discover and eliminate the grey hole and black hole attacks on the VANETs [16], where data are divided into equal blocks, and these blocks are then sent to the destination node by different routes instead of sending every data along the same route. The destination node examines the size of the sent data block; if the system detects differences in the size of the received data, it can identify the malicious route. At this point, the source node will be used to prevent data from being sent via the malicious route [16]. [PERSON] et al. developed traditional IDS for detecting cyber-attacks on robotic vehicles [17]. Their IDS is based on a decision tree method that relies on physical features and cyber in detecting malware codes. The system is evaluated by injecting two types of malicious codes in different scenarios including DoS. [PERSON] et al. designed an accurate and lightweight intrusion detection framework called AECFV [18]. It has the ability to detect the most dangerous attacks such as black hole, packet duplication wormhole, selective forwarding, Sybile and resource exhaustion attacks on VANETs. Their security system is based on a clustering approach to provide sufficient protection for VNETs. The vehicle's trust-level and nodes' mobility were used in elected Cluster-Heads (CHs), the authors also used various performance metrics in evaluating the proposed system. [PERSON] et al. proposed an intelligent IDS to identify malicious behaviour to secure the external communication for self-driving and semi self-driving vehicles [1]. Fuzzy Petri Nets were employed in this system that is particularly suited for dropping attack detection. Packet delivery rate, end-to-end delay and throughput were utilised to measure the performance of the proposed security system. [PERSON] et al. propose a prevention and detection system to identify abnormal behaviour of vehicles [19]. This security system has the ability to predict the vehicle's future behaviour and is based on Kalman filters that have the ability to predict vehicles' behaviour. [PERSON] et al. created a new security scheme to protect VANETs from possible attacks [8], which is based on the verification of the CAMs. This scheme helps researchers to decrease the number of attacks and tackle threats. [PERSON] et al. proposed two systems to detect intruders: anomaly based detection and misuse based detection [20]. Both rely on using features of the network to train an IDS that can identify intruders and a potential attacks on an ad hoc network. [PERSON] et al. propose Cross Layer Intrusion Detection (CLID) to protect wireless mesh networks (WMNs) from rushing attacks [12]. CLID was created at the network layer and the MAC layer to decrease the false alarm rate. This cooperative intrusion system assessed the proposed security system with network simulator. [PERSON] et al.