Machine learning approach for detection of nonTor traffic

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)

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.
LanguageEnglish
Title of host publicationARES '17 Proceedings of the 12th International Conference on Availability, Reliability and Security
Place of PublicationNew York
Number of pages6
DOIs
Publication statusPublished - 29 Aug 2017

Fingerprint

Intrusion detection
Learning systems
Neural networks
Support vector machines
Tunnels
Classifiers
Internet
Industry

Keywords

  • artificial neural network
  • support vector machines
  • intrusion detection systems
  • Tor
  • nonTor
  • UNB-CIC Tor network traffic dataset

Cite this

Hodo, E., Bellekens, X., Iorkyase, E., Hamilton, A., Tachtatzis, C., & Atkinson, R. (2017). Machine learning approach for detection of nonTor traffic. In ARES '17 Proceedings of the 12th International Conference on Availability, Reliability and Security [85] New York. https://doi.org/10.1145/3098954.3106068
Hodo, Elike ; Bellekens, Xavier ; Iorkyase, Ephraim ; Hamilton, Andrew ; Tachtatzis, Christos ; Atkinson, Robert. / Machine learning approach for detection of nonTor traffic. ARES '17 Proceedings of the 12th International Conference on Availability, Reliability and Security . New York, 2017.
@inproceedings{ba0af6e0e2dc4941893cec089d02c141,
title = "Machine learning approach for detection of nonTor traffic",
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.",
keywords = "artificial neural network, support vector machines, intrusion detection systems, Tor, nonTor, UNB-CIC Tor network traffic dataset",
author = "Elike Hodo and Xavier Bellekens and Ephraim Iorkyase and Andrew Hamilton and Christos Tachtatzis and Robert Atkinson",
year = "2017",
month = "8",
day = "29",
doi = "10.1145/3098954.3106068",
language = "English",
isbn = "9781450352574",
booktitle = "ARES '17 Proceedings of the 12th International Conference on Availability, Reliability and Security",

}

Hodo, E, Bellekens, X, Iorkyase, E, Hamilton, A, Tachtatzis, C & Atkinson, R 2017, Machine learning approach for detection of nonTor traffic. in ARES '17 Proceedings of the 12th International Conference on Availability, Reliability and Security ., 85, New York. https://doi.org/10.1145/3098954.3106068

Machine learning approach for detection of nonTor traffic. / Hodo, Elike; Bellekens, Xavier; Iorkyase, Ephraim; Hamilton, Andrew; Tachtatzis, Christos; Atkinson, Robert.

ARES '17 Proceedings of the 12th International Conference on Availability, Reliability and Security . New York, 2017. 85.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Machine learning approach for detection of nonTor traffic

AU - Hodo, Elike

AU - Bellekens, Xavier

AU - Iorkyase, Ephraim

AU - Hamilton, Andrew

AU - Tachtatzis, Christos

AU - Atkinson, Robert

PY - 2017/8/29

Y1 - 2017/8/29

N2 - 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.

AB - 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.

KW - artificial neural network

KW - support vector machines

KW - intrusion detection systems

KW - Tor

KW - nonTor

KW - UNB-CIC Tor network traffic dataset

U2 - 10.1145/3098954.3106068

DO - 10.1145/3098954.3106068

M3 - Conference contribution book

SN - 9781450352574

BT - ARES '17 Proceedings of the 12th International Conference on Availability, Reliability and Security

CY - New York

ER -

Hodo E, Bellekens X, Iorkyase E, Hamilton A, Tachtatzis C, Atkinson R. Machine learning approach for detection of nonTor traffic. In ARES '17 Proceedings of the 12th International Conference on Availability, Reliability and Security . New York. 2017. 85 https://doi.org/10.1145/3098954.3106068