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2015 | OriginalPaper | Buchkapitel

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

verfasst von : Mee Lan Han, Jin Lee, Ah Reum Kang, Sungwook Kang, Jung Kyu Park, Huy Kang Kim

Erschienen in: Internet of Vehicles - Safe and Intelligent Mobility

Verlag: Springer International Publishing

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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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Fußnoten
2
Engine-related = Intake air pressure (kPa) \(+\) Calculated load value (%) \(+\) Engine torque \(+\) Accelerator position (%) \(+\) Flywheel torque (Nm) \(+\) Fuel consumption (mcc).
 
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Metadaten
Titel
A Statistical-Based Anomaly Detection Method for Connected Cars in Internet of Things Environment
verfasst von
Mee Lan Han
Jin Lee
Ah Reum Kang
Sungwook Kang
Jung Kyu Park
Huy Kang Kim
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-27293-1_9