This study investigates the influence of modified Tor traffic on the classification of Onion Services, unraveling the challenges associated with evolving Darknet activities. Employing a diverse set of machine learning algorithms, including Decision Trees, Naive Bayes, Gradient Boosting, and Clustering-based methods, we explore their effectiveness in discerning subtle modifications in Tor protocols. Through comprehensive experiments and evaluation metrics, our findings shed light on the robustness and adaptability of these algorithms in the face of sophisticated traffic manipulation. The results not only contribute to a deeper understanding of Darknet dynamics but also provide valuable insights for developing more resilient and responsive cybersecurity measures. This research advances the discourse on the intersection of machine learning and cybersecurity, emphasizing the need for innovative approaches to counter the evolving threats posed by modified Tor traffic in the realm of Onion Services.

Keywords: Darknet Traffic Analysis, Tor Network, Onion Services, Traffic Classification.