Rule-Based eXplainable Autoencoder for DNS Tunneling Detection
Giacomo De Bernardi, Giovanni Battista Gaggero, Fabio Patrone, Sandro Zappatore, Mario Marchese, Maurizio Mongelli · Computers · 2025
Artificial Intelligence (AI) and Machine Learning (ML) are employed in numerous fields and applications. Even if most of these approaches offer a very good performance, they are affected by the “black-box” problem. The way they operate and make decisions is complex and difficult for human users to interpret, making the systems impossible to manually adjust in case they make trivial (from a human viewpoint) errors. In this paper, we show how a “white-box” approach based on eXplainable AI (XAI) can be applied to the Domain Name System (DNS) tunneling detection problem, a cybersecurity problem already successfully addressed by “black-box” approaches, in order to make the detection explainable. The obtained results show that the proposed solution can achieve a performance comparable to the one offered by an autoencoder-based solution while offering a clear view of how the system makes its choices and the possibility of manual analysis and adjustments.