Deep learning

Physics

Vehicular ad hoc network

Computer network

Wireless

Broadcasting (networking)

Image (mathematics)

Data mining

Geodesy

Machine learning

Anomaly (physics)

Benchmark (surveying)

Artificial intelligence

Thresholding

Geography

Condensed matter physics

Computer science

Real-time computing

Wireless ad hoc network

Artificial neural network

Anomaly detection

Telecommunications

IEEE Transactions on Vehicular Technology

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 only. However, since there can be a multitude of anomalies possible in the network, 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) using a sequence reconstruction and thresholding algorithm. 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 message sequence 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. Our best performing deep learning-based scheme detects anomalous sequences with an accuracy of 98%, a great improvement over the set benchmark.

DeepADV: A Deep Neural Network Framework for Anomaly Detection in VANETs