Cybersecurity in Intelligent Transportation Systems: A Comparative Study on AI-Based Anomaly Detection and Threat Analysis
Yazarlar (4)
Recep Arslan Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Prof. Dr. Turgut ÖZSEVEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Metin Mutlu Aydın Ondokuz Mayıs Üniversitesi, Türkiye
Yasin Çelik Cardiff University, Türkiye
| Makale Türü |
Özgün Makale
(Diğer hakemli uluslarası dergilerde yayınlanan tam makale)
|
||
| Dergi Adı | Mechatronics and Intelligent Transportation Systems | ||
| Dergi ISSN | 2958-020X | ||
| Dergi Tarandığı Indeksler | Scilit | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2026 |
| Cilt / Sayı / Sayfa | 5 / 1 / 11–30 | DOI | 10.56578/mits050102 |
| Makale Linki | https://doi.org/10.56578/mits050102 | ||
| UAK Araştırma Alanları |
Bilgi Güvenliği ve Kriptoloji
|
||
| Özet |
| The rapid integration of technology, with increasing speeds, has transformed vehicles into cyber-physical systems by connecting them to each other Vehicle-to-Everything (V2X), significantly expanding the attack surface and leaving them vulnerable to network-based threats. Current cyber intrusion detection systems (CIDS) exhibit performance degradation due to significant class imbalance, limited resilience against adversarial attacks, and insufficient interpretability for security-critical environments. To overcome the identified issues in this study, we propose Hierarchical Classifier-Agnostic Boosted Stacking for Network Intrusion Detection (HCABS-NID), a hierarchical classifier-agnostic boosted stacking architecture for network intrusion detection in connected device ecosystems. The proposed framework adds the Synthetic Minority Over-sampling Technique for Nominal and Continuous features (SMOTENC)-based adaptive class balancing to increase minority attack detection and TreeSHAP to make it multi-level interpretable. As a hierarchical stacking strategy, a two-layer structure includes heterogeneous learners together with meta-learning, calibrated with LightGBM, XGBoost, CatBoost, and TabNet to take advantage of the complementary decision boundaries. Extensive experiments performed on the benchmark dataset from University of New South Wales Network-Based 15 (UNSW-NB15) should enhance generalization performance. HCABS-NID achieved 98.20% accuracy, 97.10% macro F1 score, and 0.989 macro Receiver Operating Characteristic Area Under the Curve (ROC-AUC), in contrast to the latest community-based methods … |
| Anahtar Kelimeler |
| Adversarial robustness | Connected vehicles | Explainable artificial intelligence | Hierarchical stacking ensemble | Intelligent transportation systems | Internet of Things security | Network intrusion detection | Vehicle-to-Everything security |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |
| Google Scholar | 3 |