# Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

arXiv: https://arxiv.org/abs/1910.08635 · HTML: https://mlsanity.com/paper/1910.08635/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 25 of 25)

- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021)
- [PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams](https://mlsanity.com/paper/2109.05013/) (2021)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
- [A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2201.11812/) (2022)
- [AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2206.15476/) (2022)
- [Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.11514/) (2021)
- [An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2201.09172/) (2022)
- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles](https://mlsanity.com/paper/2208.03399/) (2022)
- [Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs](https://mlsanity.com/paper/2401.10724/) (2024)
- [STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles](https://mlsanity.com/paper/2204.10990/) (2022)
- [A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs](https://mlsanity.com/paper/2401.10689/) (2024)
- [Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN](https://mlsanity.com/paper/2401.11030/) (2024)
- [Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System](https://mlsanity.com/paper/2511.08491/) (2025)
- [An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data](https://mlsanity.com/paper/2109.13828/) (2021)
- [Anomaly Detection in Intra-Vehicle Networks](https://mlsanity.com/paper/2205.03537/) (2022)
- [Forensic Data Analytics for Anomaly Detection in Evolving Networks](https://mlsanity.com/paper/2308.09171/) (2023)
- [Optimized Ensemble Model Towards Secured Industrial IoT Devices](https://mlsanity.com/paper/2401.05509/) (2024)
- [ADVENT: Attack/Anomaly Detection in VANETs](https://mlsanity.com/paper/2401.08564/) (2024)
- [Deep Learning-based Embedded Intrusion Detection System for Automotive CAN](https://mlsanity.com/paper/2401.10674/) (2024)
- [A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN](https://mlsanity.com/paper/2401.12234/) (2024)
- [SecCAN: An Extended CAN Controller with Embedded Intrusion Detection](https://mlsanity.com/paper/2505.14924/) (2025)

## Abstract (arXiv)

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously.