Tor Network Detection by Using Machine Learning and Artificial Neural Network

The Internet is a virtual world where everyone can express themselves as much as they wish and perform the operations they want. In this virtual world, some users want to experience the internet without giving their identity for certain reasons. The concept of an anonymous network has emerged so that they can use the internet without revealing their identity. The Tor project is a product that provides anonymous communication on the Internet without revealing users' identities. In this project, we aimed to determine whether network traffic is the TOR network by using machine learning and artificial neural networks. With the dataset we have, we first performed data analysis and gained more information about the data set. Categorical values were assigned to numerical values to learn the dataset. After converting the categorical data to numerical data, normalization is applied to the data set and all features are taken between -1 and 1. It was estimated whether the future traffic was TOR by learning the past data by using K Nearest Neighbor, Naive Bayes Classifiers, Random Decision Forest, Logistic Regression, Support Vector Machine, one of the machine learning classification algorithms. In addition, artificial neural networks were used. After each algorithm, confusion matrix, precision, recall, and F1-score values, which are among the model evaluation tools, were calculated, and compared which model performed better for our dataset.