# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 1 Introduction
By exchanging vital information regarding emergency alerts, road hazards, and real-time traffic conditions, VANETs enable vehicles to connect easily with infrastructure elements (V2I) and with each other (V2V). The communicated sensitive information must be safeguarded against potential privacy and security breaches in the communication channel because both V2V and V2I communications use public channels. The use of authentication procedures by VANETs is essential for building trust between automobiles and infrastructure [16]. However, many aspects of authentication in a VANET require further enhancement and pose real challenges. In general, security in this type of network is a true problem owing to the highly dynamic topology of VANETs. Owing to the dynamic nature of these networks, they are exposed to distributed malicious attacks. Therefore, VANETs require several security measures to ensure their consistency and integrity. Before deploying an application reliant on VANETs, the security problem must be carefully considered as one of the main challenges. Try to imagine whether a safety message sent by a VANET system was altered, delayed, or deleted as a result of any kind of attack brought on by an intruder or attacker, and the grave outcomes, including accidents, fatalities, damage to infrastructure, etc. In general, integrity, availability, confidentiality, non-repudiation, accountability, and authentication are security requirements that must be satisfied to preserve the security of VANETs. Even though standard wired networks and VANETs have similar security goals, security attacks in VANETs demand unique solutions with a low computing overhead, considering the high mobility and quick dynamic changes in VANET topology. This study provides in-depth information on VANET vulnerabilities, attack classification, authentication, and countermeasures. In particular, it examines the most advanced machine learning (ML) techniques that have recently been presented to identify VANET attacks. Artificial intelligence (AI) is widely used in various application domains because of its ability to supplement conventional data-driven approaches [17; 18]. Accordingly, the authors introduced a lightweight ML approach based on a Random Forest (RF) classifier to identify VANET attacks. The contributions of this study are summarized as follows:  
* It offers thorough information on VANET systems, vulnerabilities, and attack classification. * It demonstrates the drawbacks of common authentication approaches. Authentication accuracy is still a key issue in VANETs, which is a great challenge. * It investigates the recent ML and deep learning (DL) techniques that have been proposed to counter VANET attacks with focus on their advantages and drawbacks. * It improves the CICIDS2017 dataset using the gain information feature selection technique. This significantly enhances the prediction performance, as only 61 out of 79 features were considered. * It develops a balanced version of the CICIDS2017 dataset using a random oversampling technique. An imbalanced dataset leads to bias in classification accuracy and erroneous prediction, which affects classification performance. The developed dataset, BCICIDS2017-GI, exhibited significant improvements in classification accuracy and performance. * It presents a lightweight ML model capable of precisely identifying VANET attacks with an outstanding accuracy rate and manageable overhead. It outperformed the other relevant classification methods. The proposed model is based on an RF classification model with a gain information feature selection technique. * It compared the suggested model with several reputable classification models, which exhibited better performance. In addition, the proposed detection model was compared with recent classification approaches and surpassed all of them in terms of prediction accuracy, as indicated in the Related Work section. * It offers a comprehensive analysis of the experimental findings.