Machine Learning Approach for Detection of nonTor Traffic

TL;DR

This paper compares artificial neural networks and support vector machines for detecting non-Tor traffic within Tor networks, demonstrating the neural network's superior accuracy in a specific dataset.

Contribution

It presents a comparative analysis of neural networks and SVMs for non-Tor traffic detection, highlighting the effectiveness of a hybrid neural network approach.

Findings

Both algorithms detect non-Tor traffic effectively.

Hybrid neural network outperforms SVM in accuracy.

Results based on accuracy, detection rate, false positives.

Abstract

Intrusion detection has attracted a considerable interest from researchers and industries. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymising the identity of internet users connecting through a series of tunnels and nodes. This work focuses on the classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users. A study to compare the reliability and efficiency of Artificial Neural Network and Support vector machine in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset is presented in this paper. The results are analysed based on the overall accuracy, detection rate and false positive rate of the two algorithms. Experimental results show that both algorithms could detect nonTor traffic in the dataset. A hybrid Artificial neural network proved a better classifier than SVM in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset.