Darknet Traffic Classification Using the XAI Framework

Darknet traffic classification is challenging due to the use of different obfuscation techniques. In this paper, we rely on Explainable Artificial Intelligence (XAI) techniques such as LIME and SHAP to identify the most relevant features of Darknet traffic, and use combinations of these features to train deep learning models such as Convolutional Neural Networks and Long-Short Term Memory neural networks for classification. Our results on realistic datasets lead to a classification accuracy of 95% when pertinent features are used, compared to only 87% when all features considered; thus increasing the classification accuracy while reducing the cost.