Machine Learning-Based Anomaly Detection for Traffic in IoT-Enabled Transportation Networks

Abstract

The rapid proliferation of Internet of Things (IoT) devices and their integration into increasingly interconnected applications have substantially expanded the attack surface of modern networked systems. The heterogeneous nature and high volume of IoT traffic make timely and reliable identification of malicious activities increasingly important for maintaining the security and resilience of IoT-enabled environments. This study investigates the effectiveness of maching learning approaches for supervised malicious traffic classification in IoT networks using the ACI-IoT-2023 dataset. A comparative experimental study is conducted across binary and eleven-class classification tasks to examine the capability of different learning approaches to distinguish benign and malicious traffic and identify diverse attack categories. The results demonstrate strong classification performance across the evaluated approaches, with XGBoost achieving the highest ROC-AUC in binary classification and the Decision Tree delivering the best overall performance in eleven-class classification. Further analysis of feature importance identifies several flow-level features that contribute substantially to classification performance. Overall, the findings demonstrate the effectiveness of machine learning-based approaches for accurate and efficient malicious traffic classification in IoT networks.