| Title | SHIELDNET: An Adaptive Detection Mechanism against Vehicular Botnets in VANETs |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Garip, Mevlut Turker, Lin, Jonathan, Reiher, Peter, Gerla, Mario |
| Conference Name | 2019 IEEE Vehicular Networking Conference (VNC) |
| Date Published | dec |
| Keywords | adaptive detection mechanism, Botnet, botnets, collision avoidance, compositionality, computer network security, efficiency 77.0 percent, inter-vehicular communications, Internet, Internet botnets, Intrusion detection, invasive software, learning (artificial intelligence), machine learning, machine learning algorithms, Metrics, Protocols, pubcrawl, Reputation-Based Security, Resiliency, road safety, SHIELDNET, Standards, VANET Security, vehicular ad hoc networks, vehicular botnet attacks, vehicular botnet communication, vehicular botnets, vehicular bots |
| Abstract | 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. |
| DOI | 10.1109/VNC48660.2019.9062790 |
| Citation Key | garip_shieldnet_2019 |
SHIELDNET: An Adaptive Detection Mechanism against Vehicular Botnets in VANETs