# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 1 Introduction  
Vehicular ad hoc networks (VANETs) have emerged as a critical component of intelligent transportation networks [1, 2, 3]. Vehicular ad hoc networks (VANET) seek to ensure safe driving by enhancing traffic flow, thereby lowering the number of accidents. They provide a driver or vehicle with the necessary information to help avoid these accidents. However, any modification to this real-time information could result in a system failure that compromises driver safety. Keeping this information secure is the primary priority for security professionals and researchers because it guarantees the smooth operation of VANETs [4]. A VANET is a unique type of mobile ad hoc network (MANET) [5; 6; 7; 8; 9] with pre-established routes or roads. Roadside units (RSUs) and on-board units (OBUs) are two distinct authorities that are required for registration and management. OBUs are installed in vehicles and RSUs are widely used on road edges to carry out certain services. Every vehicle can travel freely on a road network and communicate with other vehicles, RSUs, and designated authorities [4]. Because VANETs are wireless communication systems that are difficult to secure, it is imperative to protect against misuse activities and carefully specify the security architecture [10]. Owing to the exposed nature of the wireless medium [11], VANETs are vulnerable to a wide range of attacks. These attacks affect the VANET's functionality and cause challenging issues for drivers who operate legitimately. As a result, safeguarding a VANET from interception, alteration, and message deletion has proven to be a difficult task and is a top concern for both academia and industry. Messages used to guide drivers may be changed by malicious nodes. Furthermore, attackers can disseminate false information, resulting in massive accidents and damages [12]. Several years ago, many researchers investigated security attacks and sought relevant remedies. Others have attempted to codify standards and protocols or define the security infrastructure. However, there is still much to learn about trends in misbehavior detection and node trustworthiness [4]. Owing to their significant role in several applications, VANETs have attracted the interest of both the general public and the scientific community. These applications include lane merging and intersection alert signal functionality, value-added services (i.e., providing drivers access to the Internet to enhance their travel experience), toll payment services, traffic avoidance warning messages, navigation, road conditions, emergency vehicle alarm signals, and detour notifications [13]. VANET security has garnered considerable attention because of concerns regarding human safety when driving. The maintenance of availability is one of the most crucial security considerations. There will be significant harm if the VANET services are unavailable. Denial of service (DoS) assaults are therefore seen as one of the major threats that might harm VANETs. [PERSON] et al. [14] indicated that traditional security vulnerabilities in connected vehicle systems include DoS attacks. The methods used to execute these attacks against automobiles have evolved rapidly as the number of intelligent vehicles and on-board devices linked to the network has increased. Among these risks, distributed denial of service (DDoS) attacks pose a severe risk to driving safety as they can directly disrupt the availability of connected vehicles, infrastructure, and network services by sending malicious data from a number of compromised devices. [PERSON] et al. [15] indicated that DDoS attacks are the primary concern affecting VANET availability. Sufficient security measures should be offered to prevent this type of attack. To improve road safety, traffic management, and overall vehicular connections, VANETs constitute crucial technological advancements at the intersection of communication and transportation systems.