Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

Abstract

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance to extract and estimate the traffic matrix. Then, reinforcement learning is used to implement anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

Road-Side Unit Anomaly Detection

“…In [18], a CNN is used to extract the spatial, temporal, and spatio-temporal traffic features then used for anomaly detection by the means of employing thresholds. In [19], an anomaly detection approach that takes into account the spatio-temporal features of VANET traffic is proposed. The approach consists of two phases; first, deep learning based on a CNN architecture is used for network traffic estimation; second, a decision-making approach based on reinforcement learning is used to identify the normal and anomalous traffic entries.…”

Monitoring and Identification of Abnormal Network Traffic by Different Mathematical Models

“…Roselin et al [5] used an optimized deep clustering (ODC) algorithm combined with a deep autoencoder to detect malicious network traffic and found through experiments that the method performed well. Nie et al [6] proposed a method combining convolutional neural networks and reinforcement learning for anomaly detection of in-vehicle self-organizing networks and found that it was effective through experiments. Li et al [7] proposed a model integrated temporal and spatial features using a three-layer parallel network structure, conducted experiments on ISCX-IDS 2012 and CICIDS 2017 datasets, and found that the method improved detection accuracy.…”