Abstract
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.
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Engine-related = Intake air pressure (kPa) \(+\) Calculated load value (%) \(+\) Engine torque \(+\) Accelerator position (%) \(+\) Flywheel torque (Nm) \(+\) Fuel consumption (mcc).
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This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-TB1403-00.
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Han, M.L., Lee, J., Kang, A.R., Kang, S., Park, J.K., Kim, H.K. (2015). A Statistical-Based Anomaly Detection Method for Connected Cars in Internet of Things Environment. In: Hsu, CH., Xia, F., Liu, X., Wang, S. (eds) Internet of Vehicles - Safe and Intelligent Mobility. IOV 2015. Lecture Notes in Computer Science(), vol 9502. Springer, Cham. https://doi.org/10.1007/978-3-319-27293-1_9
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DOI: https://doi.org/10.1007/978-3-319-27293-1_9
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