# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 3 Related Work
### Machine Learning  
VANETs are susceptible to a wide range of security threats and vulnerabilities [36] owing to the variety of communication schemas and the inherent characteristics of wireless communications. These networks are vulnerable to novel and targeted attacks that use features unique to vehicles and the standard flaws in wireless environments. For VANETs, the majority of the security solutions designed for traditional networks are inappropriate. Therefore, to support the characteristics of vehicle networks and offer strong security procedures, researchers are searching for suitable systems. Numerous security countermeasures have been proposed, including cryptographic algorithms, traceability techniques, anonymity, and key-management systems. Recently, many studies have demonstrated that the efficiency of intrusion detection systems in identifying VANET attacks can be improved by incorporating artificial intelligence (AI). IDSs are popular methods that look for signs of security breaches in traffic and raise alarms whenever a security abnormality is noticed. Furthermore, ML can be used to create anomaly-based detection systems that can recognize unknown and zero-day threats, learn from and train themselves by examining network behavior, and gradually improve detection accuracy. The application of ML algorithms for intrusion detection in VANETs is of special interest, given the volume of data that is transferred and the variety of attacks that can occur. In recent years, many published datasets have described actual VANET communication traces, making it possible to evaluate the effectiveness of ML algorithms [20]. ML algorithms are a major contributor to the detection of many security attacks and can effectively meet current real-world requirements in the security field [37; 38] and many other fields such as health [39] and machine translation [40; 41]. Nevertheless, any attempt to mimic normal traffic causes ML algorithms to either fail to detect attacks or to become less accurate. Furthermore, ML algorithms have several flaws that allow attackers to launch complex attacks. Thus, it is imperative that weaknesses in ML techniques be evaluated in the early stages of development [42]. The extracted features of a detection model will not work if an attacker can evade the statistical analysis of VANET attacks. As a result, it is important to emphasize that more contributions to the ML domain are required for further enhancement. [PERSON] et al. [43] pointed out that securing VANETs against DDoS attacks is a challenge. The use of encryption and authentication intrusion detection techniques that incorporate broad or general mitigation measures is unreliable or problematic because these networks operate in real time. These intrusion detection techniques reduce the dimensions of the supposed data by concentrating only on the payload of the data. However, AI and ML techniques are less affected by encryption and preserve the vehicular space and have been adopted to identify and categorize different types of distributed malicious intent on VANET and their technologies. [PERSON] et al. [44] proposed a hybrid IDS for preventing cybersecurity attacks on VANET. Three approaches were integrated. These approaches include event-specific trust, rule-based filtering systems, and prior knowledge. While Bayesian Learning (BL) is used to update the risk level of attacks using the database's historical data and attack categorization model, the Dempster-Shafer theory is used to compute the risk of attacks by integrating various pieces of evidence. Comparative studies have demonstrated the efficacy of the proposed IDS through a series of tests. However, their proposed method is limited to identifying known attacks; therefore, the authors' future work will incorporate DL techniques such that their proposed system can identify new attacks. In addition, the proposed IDS requires a high detection time for attacks [45]. [PERSON] et al. [45] presented an IDS that can identify known and unknown attacks targeting VANETs.