TITLE:
Misbehavior Detection Method by Time Series Change of Vehicle Position in Vehicle-to-Everything Communication
AUTHORS:
Toshiki Okamura, Kenya Sato
KEYWORDS:
Connected Vehicle, V2X Communication, Security, Misbehavior Detection, Anomaly Detection
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.11 No.2,
April
29,
2021
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.