# Detection of malicious data in vehicular ad-hoc networks for traffic signal control applications

## TL;DR

This paper presents a novel detection method for malicious data in vehicular ad hoc networks, combining driver behavior modeling and position verification to improve traffic signal control reliability.

## Contribution

It introduces a new detection approach that effectively identifies malicious data from Sybil attacks in vehicular networks for traffic management.

## Key findings

- Method effectively detects malicious data in simulations
- Reduces negative impact of malicious data on traffic signals
- Improves traffic flow and safety in urban networks

## Abstract

Effective applications of vehicular ad hoc networks in traffic signal control require new methods for detection of malicious data. Injection of malicious data can result in significantly decreased performance of such applications, increased vehicle delays, fuel consumption, congestion, or even safety threats. This paper introduces a method, which combines a model of expected driver behaviour with position verification in order to detect the malicious data injected by vehicle nodes that perform Sybil attacks. Effectiveness of this approach was demonstrated in simulation experiments for a decentralized self-organizing system that controls the traffic signals at multiple intersections in an urban road network. Experimental results show that the proposed method is useful for mitigating the negative impact of malicious data on the performance of traffic signal control.