Paper Info
Title
A Neural Network Based User Identification For Tor Networks: Comparison Analysis Of Different Activation Functions Using Friedman Test
Abstract
In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP) metric and activation function. We analyze the data using Friedman test. From the results, we adopt null hypothesis H-0 since p < 0.05 for all activation functions. However, the softsign/x has the smallest p-value among activation functions. Therefore, it is better to use softsign/x for bad user identification in Tor networks.
Year
DOI
Venue
2016
10.1109/NBiS.2016.24
PROCEEDINGS OF 2016 19TH INTERNATIONAL CONFERENCE ON NETWORK-BASED INFORMATION SYSTEMS (NBIS)
Keywords
Field
DocType
Neural Networks, Activation Function, Friedman Test, User Identification, Intrusion Detection, Tor Networks, Deep Web
Friedman test,Network analyzer (electrical),Data mining,Data browsing,Computer science,Activation function,Network packet,Computer network,Deep Web,Artificial neural network,Intrusion detection system
Conference
ISSN
Citations 
PageRank 
2157-0418
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Tetsuya Oda144586.37
Ryoichiro Obukata2168.48
Masafumi Yamada313.81
Masahiro Hiyama415619.37
Leonard Barolli51179144.22
Makoto Takizawa63180440.50