Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles

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dc.contributor.authorTüfekcioğlu, Emre
dc.contributor.authorHanilçi, Cemal
dc.contributor.authorGürkan, Hakan
dc.date.accessioned2026-02-08T15:04:47Z
dc.date.available2026-02-08T15:04:47Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractWith the rapid advancement of digitalization and automation, modern vehicles, especially in the light commercial segment, have evolved into complex, interconnected platforms resembling mobile computing systems. This transformation has increased the dependency on in-vehicle communication networks and, as a result, exposed them to a wider range of cybersecurity threats. A fundamental aspect of the proposed method is the use of a lightweight CNN model specific for deployment in embedded automotive environments with limited computational resources and optimized for efficiency. Operating on low-power hardware platforms such as edge ECUs, the tiny device developed in this study works effectively unlike conventional deep learning architectures seeking high processing power and memory. Despite its minimal computational footprint, the model is capable of accurately distinguishing between legitimate and spoofed communication traffic, as well as detecting a variety of attack forms that target different CAN protocol components. The performance metrics of the model further highlight its effectiveness, achieving a ROC AUC Score of 0.9887, an Accuracy of 0.9887, a Precision of 0.9825, a Recall of 0.9952, and an F1-Score of 0.9888. Particularly for real-time on-vehicle intrusion detection systems, this harmony between performance and efficiency makes the strategy especially important. Just as importantly is the introduction of a specifically produced hybrid dataset, which is fundamental for system evaluation and training. The dataset aggregates synthetic generated attack scenarios with real-world spoofing, injection, and denial-of- service (DoS) conditions using actual CAN traffic acquired from a J1939-compliant light commercial vehicle. Standard 11-bit identities combined with industrial communication protocols help the dataset to reflect real-world vehicle dynamics across several ECUs under various scenarios. The model can learn fine-grained patterns often missed by conventional rule-based or manually engineered approaches by means of the image-like transformation of CAN messages—preserving bit-level and temporal information. In intelligent transportation systems, the lightweight CNN architecture and the strong dataset combine to create a scalable and deployable IDS framework that can improve in-vehicle cybersecurity.
dc.identifier.doi10.38088/jise.1717959
dc.identifier.endpage267
dc.identifier.issn2602-4217
dc.identifier.issue2
dc.identifier.startpage259
dc.identifier.trdizinid1370655
dc.identifier.urihttps://doi.org/10.38088/jise.1717959
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4178
dc.identifier.volume9
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherBursa Teknik Üniversitesi
dc.relation.ispartofJournal of Innovative Science and Engineering
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20260207
dc.subjectDeep Learning
dc.subjectDerin Öğrenme
dc.titleLightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles
dc.typeArticle

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