A Statistical-Based Anomaly Detection Method for Connected Cars in Internet of Things Environment

A connected car is the most successful thing in the era of Internet of Things (IoT). The connections between vehicles and networks grow and provide more convenience to users. However, vehicles become exposed to malicious attacks from outside. Therefore, a connected car now needs strong safeguard to protect malicious attacks that can cause security and safety problems at the same time. In this paper, we proposed a method to detect the anomalous status of vehicles. We extracted the in-vehicle traffic data from the well-known commercial car and performed the one-way ANOVA test. As a result, our statistical-based detection method can distinguish the abnormal status of the connected cars in IoT environment.