# A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles

arXiv: https://arxiv.org/abs/2505.16215 · HTML: https://mlsanity.com/paper/2505.16215/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 70 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [GIDS: GAN based Intrusion Detection System for In-Vehicle Network](https://mlsanity.com/paper/1907.07377/) (2019)
- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [Feature Selection via Mutual Information: New Theoretical Insights](https://mlsanity.com/paper/1907.07384/) (2019)
- [A Novel Hierarchical Intrusion Detection System based on Decision Tree and Rules-based Models](https://mlsanity.com/paper/1812.09059/) (2018)
- [Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI](https://mlsanity.com/paper/2410.09043/) (2024)
- [LLM-based Continuous Intrusion Detection Framework for Next-Gen Networks](https://mlsanity.com/paper/2411.03354/) (2024)
- [usfAD Based Effective Unknown Attack Detection Focused IDS Framework](https://mlsanity.com/paper/2403.11180/) (2024)
- [A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats](https://mlsanity.com/paper/2403.13010/) (2024)

## Abstract (arXiv)

Due to its nature of dynamic, mobility, and wireless data transfer, the Internet of Vehicles (IoV) is prone to various cyber threats, ranging from spoofing and Distributed Denial of Services (DDoS) attacks to malware. To safeguard the IoV ecosystem from intrusions, malicious activities, policy violations, intrusion detection systems (IDS) play a critical role by continuously monitoring and analyzing network traffic to identify and mitigate potential threats in real-time. However, most existing research has focused on developing centralized, machine learning-based IDS systems for IoV without accounting for its inherently distributed nature. Due to intensive computing requirements, these centralized systems often rely on the cloud to detect cyber threats, increasing delay of system response. On the other hand, edge nodes typically lack the necessary resources to train and deploy complex machine learning algorithms. To address this issue, this paper proposes an effective hierarchical classification framework tailored for IoV networks. Hierarchical classification allows classifiers to be trained and tested at different levels, enabling edge nodes to detect specific types of attacks independently. With this approach, edge nodes can conduct targeted attack detection while leveraging cloud nodes for comprehensive threat analysis and support. Given the resource constraints of edge nodes, we have employed the Boruta feature selection method to reduce data dimensionality, optimizing processing efficiency. To evaluate our proposed framework, we utilize the latest IoV security dataset CIC-IoV2024, achieving promising results that demonstrate the feasibility and effectiveness of our models in securing IoV networks.