Abstract
In recent years, research has been conducted on connected vehicles (CVs) that are equipped with communication devices and can be connected to networks. CVs share their own position information and surrounding information with other vehicles using Vehicle-to-Everything (V2X) communication. CVs can recognize obstacles on non-line-of-sight (NLoS), which cannot be recognized by autonomous vehicles, and reduce travel time to a destination by cooperative driving. Therefore, CVs are expected to provide safe and efficient transportation. On the other hand, problems of security of V2X communication by CVs have been discussed. Safe and efficient transportation by CVs is on the basis of the assumption that correct vehicle information is shared. If fake vehicle information is shared, it will affect the driving of CVs. In particular, vehicle position faking has been shown that it can induce traffic congestion and accidents, which is a serious problem. In this study, we define position faking by CV as misbehavior and propose a method to detect misbehavior on the basis of changes in vehicle position time series data composed of vehicle position information. We evaluated the proposed method using four different misbehavior models. F-measure of misbehavior models that CV sends random position information detected by the proposed method is higher than one by a related method. Therefore, the proposed method is suitable for detecting misbehavior in which the position information changes over time.
Highlights
In recent years, expectations for connected vehicles (CVs) equipped with communication devices have increased, and studies for their widespread use have been widely conducted
We define position faking by CV as misbehavior and propose a method to detect misbehavior on the basis of changes in vehicle position time series data composed of vehicle position information
True positive (TP) is the number of times the cloud correctly judged that misbehavior had taken place, false positive (FP) is the number of times that the cloud misjudged that misbehavior had taken place, and false negative (FN) is the number of times that the cloud did not recognize that misbehavior had taken place
Summary
Expectations for connected vehicles (CVs) equipped with communication devices have increased, and studies for their widespread use have been widely conducted. By sharing position information and surrounding information, CVs can recognize obstacles on non-line-of-sight (NLoS), which cannot be recognized by autonomous vehicles, and prevent accidents [1]. The connection of CVs to networks is expected to cause security problems. Past research focused on position faking among the attacks against CVs. position faking is difficult to prevent position faking by conventional vehicle security methods, and by network security methods such as pre-shared key (PSK) authentication and public key infrastructure (PKI) [7]. We define position faking by CVs as misbehavior and propose a method to detect misbehavior on the basis of changes in vehicle position time series data
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