Machine Learning for Wi-Fi Intrusion Detection: A Comparative Study of Accuracy, Explainability, and Adversarial Robustness

Abstract

The rapid advancement of artificial intelligence (AI) has sparked extensive discussions regarding its potential to enhance daily life through various applications. Among these, machine learning (ML) has emerged as a promising tool for detecting threats in Wi-Fi networks, a domain increasingly vulnerable to attacks due to the widespread use of wireless communication. This paper addresses the limitations of existing research, which often relies on standard metrics without a comprehensive analysis of model performance, explainability, and robustness. The study aims to provide an in-depth evaluation of ML models for Wi-Fi threat detection by employing metrics such as accuracy, precision, recall, and F1-score, alongside confusion matrices to assess classification effectiveness. Additionally, the research will analyze training and inference times, model sizes, and the impact of features using Shapley Additive Explanations (SHAP) values. Misclassifications will be scrutinized to identify potential errors stemming from dataset properties, emphasizing the necessity of thorough dataset preprocessing for broader applicability. Furthermore, the robustness of the models will be tested against adversarial attacks tailored for Wi-Fi detection. The findings will culminate in a comparative analysis of the models, underscoring the significance of each methodological step and the potential consequences of neglecting critical aspects. This work aims to contribute to the field by enhancing the understanding of ML applications in cybersecurity and promoting the development of more reliable and explainable detection systems.