Context-aware intrusion detection in vehicular communication networks: enhanced attack modeling and dataset - Khan, Muhammad Danish; Ta, Vinh Thong; Rafiq, Husnain; Nnamoko, Nonso (Taylor & Francis, 2025)
Vehicular Communication Networks (VCNs) are essential for autonomous vehicles and Intelligent Transportation Systems but face challenges in security vulnerabilities and data sparsity. Traditional attack models inadequately represent VCN dynamics, weakening threat detection, while existing datasets lack real-world mobility and spatiotemporal details. This study addresses these gaps by developing a comprehensive attack simulation framework, enhancing critical network attacks i.e. position spoofing, Sybil, and wormhole through realistic mobility patterns, positional dynamics, and temporal interactions. The resulting dataset contains legitimate and malicious instances: Spoofing (45,975 legitimate, 589 malicious), Wormhole (52,237 legitimate, 5,219 malicious), and Sybil (14,829 legitimate, 1,753 malicious). It includes essential vehicular-specific features such as mobility dynamics, inter-vehicle distances, and end-to-end communication patterns. For validation, machine learning algorithms, including Random Forest, K-Nearest Neighbors, and Logistic Regression were employed. Detection performance was evaluated using accuracy, precision, recall, and two F1-score variants (standard and macro). Results indicate high detection efficacy, with Random Forest achieving accuracy between 93.6% and 99.8% and F1-macro scores from 88.5% to 97.7%. Compared to previous studies lacking spatiotemporal considerations, our dataset’s enhanced realism demonstrates significant potential in advancing data-driven anomaly detection and real-world threat mitigation in dynamic vehicular environments.
The data underpinning the findings of this study are openly available in the Mendeley Data repository under the title Comprehensive Vehicular Communication Network Attack Dataset, licensed under CC BY 4.0. The dataset contains the raw data that were used for experiments leading to the results presented in the manuscript and can be accessed via the DOI: https://doi.org/10.17632/x7tdmsf27x.1 This dataset is essential for replicating all results reported in the article.
Keywords: 4605 Data Management and Data Science; 46 Information and Computing Sciences; 4604 Cybersecurity and Privacy; Machine Learning and Artificial Intelligence; Artificial Intelligence & Image Processing; 4601 Applied computing; 4602 Artificial intelligence; 4611 Machine learning