NIDS: Random Forest Based Novel Network Intrusion Detection System for Enhanced Cybersecurity in VANET's

Abstract

Network traffic data has developed into an appealing target for attacks as new network communication services have drawn more attention. Due to the enormous number of data these systems create, conventional intrusion detection systems (IDS) cannot detect intruder behaviors in large-scale network systems. To detect malicious assaults early on, an effective IDS must be able to scan massive volumes of network traffic data quickly. In order to identify various types of intrusion in network systems, this study introduces a unique distributed network IDS (NIDS). The suggested NIDS is built on a distributed random forest that can analyze huge amounts of data quickly. The two steps of the suggested method are the traffic gathering module and the attack detection module. In VANETs, the random forest method was employed for real-time DDoS attack detection. A variety of performance criteria, including accuracy, precision, recall, and F1 score, were tested on the system to confirm the performance of the suggested framework. The proposed method achieves improved classification accuracy, according to the results. The suggested approach is suitable for complicated systems with high speed and low false alarm rates that require real-time intrusion detection.