2 research outputs found
A credibility score algorithm for malicious data detection in urban vehicular networks
This paper introduces a method to detect malicious data in urban vehicular networks,
where vehicles report their locations to road-side units controlling traffic signals at intersections.
The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get
the green light immediately. Another source of malicious data are vehicles with malfunctioning
sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata,
which determines intervals representing possible positions of vehicles. A credibility score algorithm
is introduced to decide if positions reported by particular vehicles are reliable and should be taken
into account for controlling traffic signals. Extensive simulation experiments were conducted to verify
effectiveness of the proposed approach in realistic scenarios. The experimental results show that the
proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods.
The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on
the performance of traffic signal control