# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 1 Introduction  
Vehicle-to-everything (V2X) communication is a key component of intelligent transportation systems (ITSs). It enables vehicles to communicate with each other (V2V), with infrastructure (V2I), with pedestrians (V2P), and with networks (V2N) [1]. For instance, Basic Safety Messages (BSMs) [2] are the most common V2X messages, whereby vehicles broadcast to inform surrounding vehicles about their position, speed, direction, and other state information. Because they contain critical data about vehicle dynamics, BSMs are highly relevant for detecting anomalies, such as sudden deviations in driving behavior, spoofed GPS coordinates, or irregular speeds, that may suggest sensor malfunctions or data falsification attacks. This interconnected environment aims to enhance road safety, improve traffic efficiency, and enhance the driving experience [3]. However, the increased connectivity also brings significant security challenges, making V2X systems vulnerable to various cyberattacks [4]. In this paper, V2X security specifically addresses the challenge of detecting anomalies in BSMs that cyberattacks, such as data injection or manipulation, may cause. Such attacks aim to falsify kinematic information (e.g., position, speed), thereby undermining the reliability of V2X communications and posing significant risks to road safety and traffic efficiency. Key security concerns include ensuring data integrity to prevent tampering, verifying the identity of entities through robust authentication mechanisms to prevent unauthorized access, maintaining the confidentiality of sensitive data, guaranteeing the availability of V2X services amidst attacks like Denial of Service (DoS), and protecting user privacy to prevent the exposure or misuse of personal information [5]. Traditional security mechanisms such as encryption and robust authentication protocols are necessary for securing communication. It is essential to emphasize that anomaly detection systems, such as the one proposed in this work, are designed to operate as part of a defense-in-depth strategy. They complement other essential security measures, such as cryptographic mechanisms and network-level security. Our approach focuses on identifying anomalies in the semantic content of V2X messages, which might still occur even if messages are cryptographically sound. These traditional approaches alone are often inadequate in dynamic V2X environments where attacks evolve rapidly, and computational efficiency is a priority [6]. Encryption, while effectively safeguarding data confidentiality and integrity, does not inherently detect or mitigate real-time cyber threats. For instance, encrypted malicious messages can still disrupt operations or cause accidents if not identified and addressed in real time. Therefore, there is a pressing need for a robust security framework that can effectively counter these threats while maintaining the efficiency and reliability of V2X communications [7]. Generative AI offers innovative solutions to enhance the security of V2X communications, particularly through the use of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) [8]. These models excel in anomaly detection by learning the expected behavior of V2X communication patterns and flagging any deviations as potential security threats. For instance, GANs can be trained to generate synthetic network traffic that mimics normal traffic patterns, and the discriminator in the GAN can then detect abnormal traffic, indicating a possible attack. VAEs have emerged as a powerful tool for anomaly detection due to their ability to learn latent representations of normal behavior in high-dimensional data. VAEs can identify deviations that signify anomalous behavior by reconstructing input data and measuring reconstruction errors. This probabilistic framework allows VAEs to model uncertainty, making them highly effective in detecting subtle and complex anomalies that are difficult to identify using traditional methods [9].