Recent research in intrusion detection systems (IDS) for Vehicular Ad Hoc Networks focuses on developing intelligent and adaptive security frameworks capable of detecting and mitigating various cyberattacks such as denial of service, false data injection, and Sybil attacks. Researchers are integrating machine learning, deep learning, and federated learning models to improve the accuracy and scalability of intrusion detection under high mobility and dynamic network conditions. Hybrid models combining feature optimization, anomaly detection, and classification are being explored to enhance detection rates while reducing false positives. Additionally, blockchain and software-defined networking concepts are being incorporated to provide decentralized, transparent, and tamper-proof detection mechanisms. Overall, these advancements aim to create efficient, privacy-preserving, and real-time IDS solutions suitable for the complex and evolving security challenges in vehicular networks.