Name
Title | ||
|---|---|---|
Performance Evaluation of a Neural Network Based Intrusion Detection System for Tor Networks Considering different Hidden Units. |
Abstract | ||
|---|---|---|
Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect the intrusion in Tor networks. In this paper, we present the application of Neural Networks (NNs) for intrusion detection in Tor networks. We used the Back-propagation NN and constructed a Tor server and a Deep Web browser (client). 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 Backpropagation NN to make the approximation. We present many simulation results for different number of hidden units. The simulation results show that our simulation system has a good approximation and can be used for intrusion detection in Tor networks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/NBiS.2015.116 | NBiS |
Keywords | Field | DocType |
Neural Networks, Intrusion Detection, Tor Networks, Deep Web, Hidden Unit | Network analyzer (electrical),Denial-of-service attack,Computer science,Identity theft,Computer network,Hacker,Anonymity,Backpropagation,Artificial neural network,Intrusion detection system,Distributed computing | Conference |
ISSN | Citations | PageRank |
2157-0418 | 0 | 0.34 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taro Ishitaki | 1 | 7 | 3.30 |
| Tetsuya Oda | 2 | 445 | 86.37 |
| Keita Matsuo | 3 | 212 | 55.01 |
| Leonard Barolli | 4 | 1179 | 144.22 |
| Makoto Takizawa | 5 | 3180 | 440.50 |