Spatio-Temporal Network Traffic Estimation and Anomaly Detection Based on Convolutional Neural Network in Vehicular Ad-Hoc Networks

Over the last decade, vehicular ad-hoc networks (VANETs) have received a greater attention in academia and industry due to their influence in intelligent transportation systems. Providing reliability and security to the VANET is essential in order to guarantee the efficiency of its applications. Anomaly detection has become a challenging problem due to the unique environment of VANETs with quick movement and short-lived link. In this paper, a method using the spatio-temporal feature of network traffic is proposed to implement network traffic estimation at first, and on this basis, an anomaly detection algorithm is put forward. The convolutional neural network is employed to extract the spatio-temporal features of the traffic matrix. In terms of the extracted features, network traffic is estimated by using a fully connected architecture as the output layer. Then, a threshold-based separation method is used to implement anomaly detection. The preliminary experiments comparing the proposed method with other machine learning-based methods show the effectiveness of the proposed anomaly detection method.