mlsanityData to 7 Oct 2026

Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

arXiv 1910.08635 ↗First posted 18 Oct 2019cs.LG cs.CR stat.MLcited by 25

Not on the map. Why?

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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2019.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 7; 2022: 7; 2023: 1; 2024: 8; 2025: 2.

25 papers.

Sort:

  1. 2021MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehiclescited by 36
  2. 2021A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streamscited by 28
  3. 2021PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streamscited by 17
  4. 2022IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspectivecited by 15
  5. 2021Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunitiescited by 14
  6. 2022A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehiclescited by 11
  7. 2022AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detectioncited by 11
  8. 2021Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detectioncited by 9
  9. 2022An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Seriescited by 9
  10. 2024On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Casecited by 9
  11. 2021Machine Learning for Security in Vehicular Networks: A Comprehensive Surveycited by 7
  12. 2022LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehiclescited by 6
13 more
  1. 2024Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAscited by 5
  2. 2022STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehiclescited by 2
  3. 2024A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAscited by 1
  4. 2024Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CANcited by 1
  5. 2025Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection Systemcited by 1
  6. 2021An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Datacited by 0
  7. 2022Anomaly Detection in Intra-Vehicle Networkscited by 0
  8. 2023Forensic Data Analytics for Anomaly Detection in Evolving Networkscited by 0
  9. 2024Optimized Ensemble Model Towards Secured Industrial IoT Devicescited by 0
  10. 2024ADVENT: Attack/Anomaly Detection in VANETscited by 0
  11. 2024Deep Learning-based Embedded Intrusion Detection System for Automotive CANcited by 0
  12. 2024A Lightweight FPGA-based IDS-ECU Architecture for Automotive CANcited by 0
  13. 2025SecCAN: An Extended CAN Controller with Embedded Intrusion Detectioncited by 0