## 1 Introduction  
### Motivation  
Recently, Internet-of-Things (IoT) has emerged as a novel computing paradigm which provides connectivity to plethora of devices with sensing and communication capabilities to the Internet. These devices are connected to the Internet via various communication technologies including WiFi, ZigBee, Bluetooth, Long Term Evolution (LTE), 5G to provide a wide range of applications such as smart homes, smart grid, smart health, smart factories etc. [1; 2; 3]. Vehicular Ad-Hoc Network (VANET), which provides connectivity to millions of vehicles across the globe with the aim to improve traffic efficiency, is an important application of IoT and smart cities. In VANET, smart vehicles equipped with various sensors intelligently exchange messages with each other by virtue of vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication to offer various applications including traffic management, black-ice and steep-curve warnings, congestion and traffic accident avoidance. Figure 1 illustrates the integration of VANET in smart cities where traffic safety is achieved by connecting vehicles to each other by via V2V and V2I communication. Due to sensitive nature of VANET, a secure, trustworthy and attack-free environment is mandatory for disseminating messages throughout the network. Since, very critical information (such as black-ice warnings) is involved in VANET, thus it is paramount that this information is authentic and is generated from legitimate vehicles. Further, network should be capable of satisfying traditional security requirements, i.e., availability, authenticity, privacy, confidentiality, non-repudiation and integrity [4; 5; 6; 7]. However, ensuring security in VANET is extremely challenging due to its susceptibility to different attacks such as Man-In-The-Middle (MITM) attacks which pose a severe risk in VANET [8; 9]. In these attacks, malicious node (MITM) either eavesdrop or alter the messages exchanged between two legitimate vehicles. The exchanged information may contain sensitive and delay-intolerant information such as steep-curve warning. This results in the dissemination of compromised and incorrect information throughout the network, thus violating main pillars of security requirement, i.e., availability, confidentiality and integrity. Since, human life is directly involved in VANET, propagation of compromised information via malicious MITM nodes can have severe impact in the network. For instance, malicious nodes tampering steep-curve warning can result in traffic accidents. MITM attacks are considered as severe attacks in VANET where malicious nodes have the ability to alter, drop or delay useful information in the network [10]. Adversaries can launch MITM attacks in two modes, i.e., passively or actively. Passively, the attacker can eavesdrop on the communication channel silently between legitimate vehicles. For instance, MITM attacker intercepts the communication channel of law-enforcement vehicles and share the communication with the interested organization for their own benefits. Actively, the attacker can drop, delay or change the content of received information in the network. For example, the attacker receives sensitive information such as a message about a traffic accident. If the attacker either changes the content of the message, or delay or drop the message,  
Figure 1: Illustration of VANET in smart cities. then this behavior of the MITM attacker can have severe impact on the network as the legitimate nodes will either receive compromised messages, or the information is delivered with high delays, or vehicles are prohibited from legitimate information in extreme cases. Moreover, attacker pattern and strategies have significant impact on the performance of the network. As an illustration, the attacks launched by the attackers individually have different impact to the attacks launched in a collaborative manner. Thus, the performance of the network vary in both cases.