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Full title—DeepADV: A Deep Neural Network Framework for Anomaly Detection in VANETs
We are seeing a growth in the number of connected vehicles in Vehicular Ad-hoc Networks (VANETs) to achieve the goal of Intelligent Transportation System (ITS). This is leading to a connected vehicular network scenario with vehicles continuously broadcasting data to other vehicles on the road and the roadside network infrastructure.
The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. Existing works provide solutions addressing specific anomalies in the network. However, since there can be a multitude of anomalies possible, there is a need for better anomaly detection frameworks that can address this unprecedented scenario.
In this paper, we propose an anomaly detection framework for VANETs based on deep neural networks (DNNs). In this framework, the DNN architectures are deployed on the roadside units (RSUs) which receive the broadcast vehicular data and run anomaly detection tasks to classify a particular vehicle as anomalous or genuine.
Multiple DNN architectures are implemented in this experiment, and their performance is compared using key evaluation metrics. Performance comparison of the proposed framework is also drawn against the prior work in this area.
Full Article: IEEE Transactions on Vehicular Technology, Early Access | 
