A Machine Learning-Based Intrusion Detection Approach for Intelligent Connected Vehicles

The current intelligent connected vehicles (ICV) often need to share the detected intrusion events to the cloud for further collaborative investigation. However, it was found that the high volume of false alarms will bring congestion to the upstream channel, which will exhaust the bandwidth and even deny other legitimate data sharing. This paper therefore is motivated to use the machine learning-based intrusion detection approach to increase the detection performance, in particular, to reduce the false positive rate. With the effective feature selection over the datasets, our approach yields a higher detection performance and lower computational cost. Further, the experimental results show that our approach has a lower false positive rate than that of the previous works on the common datasets.