• Deepa Gianchandani
• Dr. Soni Changlani

Intrusion Detection Systems (IDS) play a vital role in ensuring the security and reliability of Vehicular Ad Hoc Networks (VANETs), which are highly dynamic and vulnerable to various cyberattacks due to their decentralized and open communication environment. Recent research has highlighted the effectiveness of Machine Learning (ML) techniques in enhancing IDS performance by enabling intelligent detection of anomalies and malicious behaviors. This review explores different ML-based approaches applied to IDS in VANETs, including supervised, unsupervised, and hybrid methods, while analyzing their strengths, limitations, and suitability for real-time applications. The study further discusses evaluation metrics, datasets, and challenges such as scalability, false alarm rates, and adaptability to evolving attack patterns. Finally, the paper emphasizes future research directions to improve ML-driven IDS for VANETs, aiming to achieve robust, efficient, and secure vehicular communication systems.

IoT, IDS, Cyber, Attack, Security, Internet, VANET

"Machine Learning Techniques of Intrusion Detection System for Vehicular Ad Hoc Networks: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.i229-i233, December-2023, Available :http://www.jetir.org/papers/JETIR2312829.pdf

"Machine Learning Techniques of Intrusion Detection System for Vehicular Ad Hoc Networks: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppi229-i233, December-2023, Available at : http://www.jetir.org/papers/JETIR2312829.pdf