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AI-Based Intrusion Detection Techniques for Vehicle Networks
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AI-Based Intrusion Detection Techniques for Vehicle Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 10 October 2025 | Viewed by 733

Special Issue Editor

Prof. Dr. Xiangxue Li

E-Mail Website
Guest Editor
School of Software Engineering, East China Normal University, Shanghai 200062, China
Interests: artificial intelligence security; blockchain; Intelligent connected vehicle; cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of intelligent transportation systems and the increasing connectivity of vehicles have made the in-vehicle network a critical component that enables the exchange of data between various electronic control units (ECUs) and external systems. However, this interconnectivity also exposes vehicles to a wide range of cybersecurity risks. Traditional security measures are often inadequate to defend against sophisticated cyberattacks. This necessitates the development of advanced intrusion detection techniques that can effectively identify and mitigate potential threats.

Artificial intelligence (AI) leverages the power of machine learning, deep learning, and other AI algorithms to indicate potential cyberattacks in in-vehicle networks. AI techniques can continuously learn and adapt to changes in vehicle behavior, improving their ability to detect and mitigate evolving cyber threats.

This Special Issue focuses on AI-based intrusion detection techniques for in-vehicle networks. We welcome research and developments which explore a wide range of topics, including anomaly detection algorithms, supervised and unsupervised learning techniques, deep learning models, and their applications in detecting different types of cyberattacks.

Prof. Dr. Xiangxue Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • intrusion detection
  • vehicle networks

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Published Papers (1 paper)

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Research

27 pages, 1846 KiB  
Article
Vision-Language Model-Based Local Interpretable Model-Agnostic Explanations Analysis for Explainable In-Vehicle Controller Area Network Intrusion Detection
by Jaeseung Lee and Jehyeok Rew
Sensors 2025, 25(10), 3020; https://doi.org/10.3390/s25103020 - 10 May 2025
Viewed by 295
Abstract
The Controller Area Network (CAN) facilitates efficient communication among vehicle components. While it ensures fast and reliable data transmission, its lightweight design makes it susceptible to data manipulation in the absence of security layers. To address these vulnerabilities, machine learning (ML)-based intrusion detection [...] Read more.
The Controller Area Network (CAN) facilitates efficient communication among vehicle components. While it ensures fast and reliable data transmission, its lightweight design makes it susceptible to data manipulation in the absence of security layers. To address these vulnerabilities, machine learning (ML)-based intrusion detection systems (IDS) have been developed and shown to be effective in identifying anomalous CAN traffic. However, these models often function as black boxes, offering limited transparency into their decision-making processes, which hinders trust in safety-critical environments. To overcome these limitations, this paper proposes a novel method that combines Local Interpretable Model-agnostic Explanations (LIME) with a vision-language model (VLM) to generate detailed textual interpretations of an ML-based CAN IDS. This integration mitigates the challenges of visual-only explanations in traditional XAI and enhances the intuitiveness of IDS outputs. By leveraging the multimodal reasoning capabilities of VLMs, the proposed method bridges the gap between visual and textual interpretability. The method supports both global and local explanations by analyzing feature importance with LIME and translating results into human-readable narratives via VLM. Experiments using a publicly available CAN intrusion detection dataset demonstrate that the proposed method provides coherent, text-based explanations, thereby improving interpretability and end-user trust. Full article
(This article belongs to the Special Issue AI-Based Intrusion Detection Techniques for Vehicle Networks)
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