A hybrid feature fusion approach for multiclass Wi-Fi intrusion detection using classical machine learning

Abstract Wi-Fi networks have become a fundamental component of Internet of Things (IoT) environments, while their open and shared nature also exposes them to a wide range of cyber attacks. This study examines the use of time-series feature engineering combined with classical machine learning techniques for multiclass Wi-Fi intrusion detection using the AWID3 dataset. Network traffic is segmented into multivariate time-series blocks to capture temporal characteristics of wireless communication. From these segments, two complementary feature representations are derived: statistical descriptors that support interpretability and CNN-based features that capture spatial and temporal patterns. The proposed framework is evaluated using K-Nearest Neighbors, Support Vector Machines, XGBoost, and ensemble voting classifiers across 24 experimental configurations, considering different sequence lengths and feature extraction strategies. The experimental results indicate that classical machine learning models, particularly XGBoost combined with statistical time-series features, achieve strong performance in a 14-class intrusion detection task, with accuracy and F1-score exceeding 0.98. These findings demonstrate that carefully designed feature representations, when paired with well-established classifiers, can provide an effective and computationally efficient solution for practical Wi-Fi intrusion detection scenarios.