Vehicular Ad Hoc Networks (VANETs), which enhance traffic efficiency and road safety, are becoming a crucial component of intelligent transportation systems. However, the dynamic and decentralized nature of VANETs makes them susceptible to numerous security threats, such as Denial of Service (DoS), Sybil attacks, and data manipulation. Existing security solutions encounter challenges in extracting features and managing intricate network dynamics, mainly because they rely on conventional cryptography methods and machine learning models. Motivated by the need for more robust security protocols, this chapter investigates the application of deep learning (DL) methods for VANET security. This chapter presents a detailed analysis of deep learning framewch as DeepVCM and DeepADV for intrusion detection and anomaly detection, respectively, demonstrating their superiority over conventional methods. Additionally, few DL-based solutions for secure communication and trust management in VANETs, including a framework for Sybil attack detection and trust computation using deep neural networks are discussed in this chapter. The comparative study of mitigation strategies highlights the effectiveness of DL in automating feature extraction and improving detection accuracy. This chapter presents multiple methods of notable breakthroughs in VANET security by examining these DL procedures, ensuring a more reliable and safe vehicular communication environment.