VANET Network Traffic Anomaly Detection Using GRU-Based Deep Learning Model

The rise of Vehicular Ad-hoc Networks (VANETs) has led to the growing significance in intelligent transportation systems. This research suggests a deep learning model for anomaly detection based on GRU over VANET network traffic to address this challenge. Consumer electronics technologies can be successfully introduced to the market in one of two ways: either there is a clear benefit for the customer from using this technology, or it is required by a regulatory order that prevents the use of alternatives. It is possible to detect unknown assaults and DoS floods using traffic anomalies. Users can keep track of the security features of multimedia services by using Traffic Anomaly Detection, which provides an overview of traffic anomaly detection analysis. Anomaly detection methods fall into three categories: unsupervised, semi-supervised, and supervised. The right anomaly detection technique basically depends on the labels that are present in the dataset. To further improve the accuracy of proposed model, a new semi-supervised technique for detecting VANET network activity anomalies called SEMI-GRU has been proposed. The results demonstrate that proposed GRU-based deep learning model outperforms existing methods in detecting network anomalies with low false positive rates.