Supervised Machine Learning for Real-Time Intrusion Attack Detection in Connected and Autonomous Vehicles: A Security Paradigm Shift

Ahmad Aloqaily, Emad E. Abdallah, Hiba AbuZaid, Alaa E. Abdallah, Malak Al-hassan

Recent improvements in self-driving and connected cars promise to enhance traffic safety by reducing risks and accidents. However, security concerns limit their acceptance. These vehicles, interconnected with infrastructure and other cars, are vulnerable to cyberattacks, which could lead to severe costs, including physical injury or death. In this article, we propose a framework for an intrusion detection system to protect internal vehicle communications from potential attacks and ensure secure sent/transferred data. In the proposed system, real auto-network datasets with Spoofing, DoS, and Fuzzy attacks are used. To accurately distinguish between benign and malicious messages, this study employed seven distinct supervised machine-learning algorithms for data classification. The selected algorithms encompassed Decision Trees, Random Forests, Naive Bayes, Logistic Regression, XG Boost, LightGBM, and Multi-layer Perceptrons. The proposed detection system performed well on large real-car hacking datasets. We achieved high accuracy in identifying diverse electronic intrusions across the complex internal networks of connected and autonomous vehicles. Random Forest and LightGBM outperformed the other algorithms examined. Random Forest outperformed the other algorithms in the merged dataset trial, with 99.9% accuracy and the lowest computing cost. The LightGBM algorithm, on the other hand, performed admirably in the domain of binary classification, obtaining the same remarkable 99.9% accuracy with no computing overhead.