Vehicular ad hoc networks (VANETs) are designed to provide traffic safety by enabling vehicles to broadcast information-such as speed, location and heading-through inter-vehicular communications to proactively avoid collisions. However, the attacks targeting these networks might overshadow their advantages if not protected against. One powerful threat against VANETs is vehicular botnets. In our earlier work, we demonstrated several vehicular botnet attacks that can have damaging impacts on the security and privacy of VANETs. In this paper, we present SHIELDNET, the first detection mechanism against vehicular botnets. Similar to the detection approaches against Internet botnets, we target the vehicular botnet communication and use several machine learning techniques to identify vehicular bots. We show via simulation that SHIELDNET can identify 77 percent of the vehicular bots. We propose several improvements on the VANET standards and show that their existing vulnerabilities make an effective defense against vehicular botnets infeasible.
SHIELDNET: An Adaptive Detection Mechanism against Vehicular Botnets in VANETs
2019-12-01
1394563 byte
Conference paper
Electronic Resource
English
AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks
ArXiv | 2024
|A-MoMAC: mobility-aware and adaptive vehicular MAC protocol in VANETs
British Library Conference Proceedings | 2022
|Secure Vehicular Crowdsensing and Malicious Vehicles Detection in VANETs
Springer Verlag | 2025
|