Towards Resilient Darknet Security: Integrating Transfer Learning and Dynamic Ensemble Techniques

The persistent nature of darknets provides a ground for illicit activities, demanding continuous advancements. This research extends the current paradigm by introducing a comprehensive evaluation of diverse deep learning & machine learning techniques, with an emphasis on the application of transfer learning, to enhance proficiency of darknet traffic detection and the classification of underlying application types. To augment the adaptability and generalizability of the detection system, transfer learning techniques are incorporated. By training the model on one darknet dataset and evaluating its performance on others, a more generalized and adaptable detection system is aimed at identifying emerging threats across different darknet environments. Additionally, to improve the transparency and interpretability of the model, explainable artificial intelligence (XAI) techniques are integrated. Furthermore, the research introduces an innovative approach by integrating dynamic ensemble learning, where the ensemble's composition adapts in real-time based on the evolving characteristics of darknet traffic. In summary, this study advances the state-of-the-art in darknet traffic detection and introduces transfer learning as a key element in developing more generalized and adaptable detection systems.