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In response to the growing cybersecurity challenges in Vehicular Ad Hoc Networks (VANETs), we designed a Network Intrusion Detection System (NIDS) based on machine learning that can better detect malicious threats. The system is based on three main algorithms: Random Forest, XGBoost, Gradient Boosting. These algorithms classify network traffic as normal or malicious. By combining these algorithms, the system exploits their collective strengths, thereby improving the detection accuracy. The NIDS has an alert function that sends notifications about possible intrusions directly to the user’s web interface. This approach will improve road safety and traffic management by strengthening vehicular communication security. The system is trained and evaluated using the NLS-KDD dataset for better detection performance in VANET environments.

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Network Intrusion Detection in VANETS Using Machine Learning: Securing VANETS with Machine Learning- Based NIDS Approach

  • 会议论文

  • 发表日期：

    2026年

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  • I. Ravi Prakash Reddy,
  • Akshaya Juluri,
  • Sanjana Reddy,
  • Pola Rishika

摘要

In response to the growing cybersecurity challenges in Vehicular Ad Hoc Networks (VANETs), we designed a Network Intrusion Detection System (NIDS) based on machine learning that can better detect malicious threats. The system is based on three main algorithms: Random Forest, XGBoost, Gradient Boosting. These algorithms classify network traffic as normal or malicious. By combining these algorithms, the system exploits their collective strengths, thereby improving the detection accuracy. The NIDS has an alert function that sends notifications about possible intrusions directly to the user’s web interface. This approach will improve road safety and traffic management by strengthening vehicular communication security. The system is trained and evaluated using the NLS-KDD dataset for better detection performance in VANET environments.