  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.
