An edge-based smart network monitoring system for the Internet of Vehicles

The Internet of Vehicles (IoV) is the future of transportation. It will be present everywhere and will have a huge impact on our lives. However, there are plenty of aspects to consider while studying these networks, such as data dissemination, cybersecurity threats and vulnerabilities. For an IoV to work efficiently, data needs to spread through it efficiently. However, the dynamics of vehicular environments due to frequent node mobility and nodes' misbehavior poses many challenges to efficient data dissemination. Therefore, a deep learning-based monitoring system that is capable of detecting anomalies in the network and identifying known misbehavior is proposed. Performance evaluation shows that the monitoring system can identify well-known attacks with a very high success rate. Besides, the algorithm is also capable of detecting other types of misbehavior without labeling them.