Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Jonggwon Kim¹, Hyungchul Im¹, Semin Kim¹

¹Department of Intelligent Semiconductors, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Adaptive Image Deblurring Convolutional Neural Network with Meta-Tuning.

Sensors (Basel, Switzerland)·2025

Same author

Adaptive Autoencoder-Based Intrusion Detection System with Single Threshold for CAN Networks.

Sensors (Basel, Switzerland)·2025

Same author

EHNet: Efficient Hybrid Network with Dual Attention for Image Deblurring.

Sensors (Basel, Switzerland)·2024

Same author

Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms.

Sensors (Basel, Switzerland)·2024

Same author

A Novel Architecture for an Intrusion Detection System Utilizing Cross-Check Filters for In-Vehicle Networks.

Sensors (Basel, Switzerland)·2024

Same author

CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations.

Sensors (Basel, Switzerland)·2023

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026

See all related articles

This study introduces a semi-supervised generative adversarial network (SGAN) for controller area network (CAN) bus intrusion detection. The SGAN framework effectively identifies unknown cyberattacks on vehicles with high accuracy and efficiency.

Area of Science:

Cybersecurity
Machine Learning
Automotive Engineering

Background:

Controller Area Network (CAN) buses are crucial for in-vehicle communication but lack security features like authentication and encryption.
Vulnerabilities in CAN buses allow for malicious attacks, compromising vehicle safety and data integrity.
Existing deep learning intrusion detection systems (IDS) face limitations with supervised methods requiring extensive labeled data and unsupervised methods yielding high false positives.

Purpose of the Study:

To propose a novel semi-supervised generative adversarial network (SGAN) framework for robust intrusion detection on CAN bus systems.
To address the limitations of existing IDSs by combining image-based CAN representation with adversarial learning for improved accuracy and efficiency.
To develop a practical cybersecurity solution for protecting safety-critical vehicular sensing and control functions.

Keywords:

automotive cybersecurity controller area network generative adversarial network intrusion detection system semi-supervised learning sensor cybersecurity vehicular sensing

Related Experiment Videos

Main Methods:

Consecutive CAN messages are transformed into 64x9 grayscale images for analysis.
A three-phase training approach is employed: initial discriminator training on labeled data, refinement using distribution-level objectives and generated samples, and generator training to produce realistic adversarial samples.
The framework utilizes a semi-supervised generative adversarial network (SGAN) integrating image-based CAN representation and adversarial learning.

Main Results:

Achieved an average accuracy of 99.73% and an F1-score of 99.63% on detecting unknown attacks using leave-one-class-out experiments on the HCRL car-hacking dataset.
Demonstrated high performance in identifying novel and unseen cyber threats within the CAN bus.
The model is computationally efficient with only 0.21 million parameters and 3.25 million floating-point operations (FLOPs), making it suitable for resource-constrained automotive platforms.

Conclusions:

The proposed SGAN framework offers a practical and effective solution for enhancing cybersecurity in connected vehicles by detecting CAN bus intrusions.
The method successfully overcomes the trade-offs associated with traditional supervised and unsupervised deep learning IDSs.
The developed intrusion detection system is efficient and accurate, paving the way for enhanced security in automotive sensing and control applications.