In order to shape and monitor traffic, it is necessary to classify network traffic. The significance of privacy-preserving technology has increased in the last twenty years due to the growth of privacy concerns. One common method of remaining anonymous while surfing the web is to join the Tor network. This will allow you to remain anonymous while also supporting anonymous services called Onion Services. The problem is that government and law enforcement organizations often take advantage of this anonymity, particularly with Onion Services, and end up de-anonym zing its users. This paper's emphasis is on three primary contributions in an effort to discover the capability to categorize Onion Service traffic. Separating Onion Service communication from regular Tor traffic is our first objective. With over 99% accuracy, our methods can detect Onion Service traffic. On the other hand, Tor traffic may have its information leaking concealed by making a Few adjustments. We assess the efficacy of our methods in light of these changes to Tor traffic in our second contribution. According to our experiments, under these circumstances, the Onion Services traffic becomes less distinct, with an accuracy decrease of over 15% seen in some instances. We conclude by determining and assessing the effect of the most important feature combinations on our classification task.