# AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks

arXiv: https://arxiv.org/abs/2407.03506 · HTML: https://mlsanity.com/paper/2407.03506/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 80 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [GIDS: GAN based Intrusion Detection System for In-Vehicle Network](https://mlsanity.com/paper/1907.07377/) (2019)
- [Light Commands: Laser-Based Audio Injection Attacks on Voice-Controllable Systems](https://mlsanity.com/paper/2006.11946/) (2020)
- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [Security and Privacy for Mobile Edge Caching: Challenges and Solutions](https://mlsanity.com/paper/2012.03165/) (2020)
- [Tracking Temporal Evolution of Network Activity for Botnet Detection](https://mlsanity.com/paper/1908.03443/) (2019)
- [Cooperative Collision Avoidance in a Connected Vehicle Environment](https://mlsanity.com/paper/2306.01889/) (2023)

## Built on by (most cited first, 3 of 3)

- [Multi-agent Reinforcement Learning-based Network Intrusion Detection System](https://mlsanity.com/paper/2407.05766/) (2024)
- [A Life-long Learning Intrusion Detection System for 6G-Enabled IoV](https://mlsanity.com/paper/2407.15700/) (2024)
- [Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization](https://mlsanity.com/paper/2504.18814/) (2025)

## Abstract (arXiv)

Connected cars offer safety and efficiency for both individuals and fleets of private vehicles and public transportation companies. However, equipping vehicles with information and communication technologies raises privacy and security concerns, which significantly threaten the user's data and life. Using bot malware, a hacker may compromise a vehicle and control it remotely, for instance, he can disable breaks or start the engine remotely. In this paper, besides in-vehicle attacks existing in the literature, we consider new zeroday bot malware attacks specific to the vehicular context, WSMP-Flood, and Geo-WSMP Flood. Then, we propose AntibotV, a multilevel behaviour-based framework for vehicular botnets detection in vehicular networks. The proposed framework combines two main modules for attack detection, the first one monitors the vehicle's activity at the network level, whereas the second one monitors the in-vehicle activity. The two intrusion detection modules have been trained on a historical network and in-vehicle communication using decision tree algorithms. The experimental results showed that the proposed framework outperforms existing solutions, it achieves a detection rate higher than 97% and a false positive rate lower than 0.14%.