Reliable Probabilistic Classification of Internet Traffic. / Dashevskiy, Mikhail; Luo, Zhiyuan.

In: International Journal of Information Acquisition, Vol. 6, No. 2, 2009, p. 133-146.

Research output: Contribution to journalArticlepeer-review

Published

Standard

Reliable Probabilistic Classification of Internet Traffic. / Dashevskiy, Mikhail; Luo, Zhiyuan.

In: International Journal of Information Acquisition, Vol. 6, No. 2, 2009, p. 133-146.

Research output: Contribution to journalArticlepeer-review

Harvard

Dashevskiy, M & Luo, Z 2009, 'Reliable Probabilistic Classification of Internet Traffic', International Journal of Information Acquisition, vol. 6, no. 2, pp. 133-146. https://doi.org/10.1142/S0219878909001837

APA

Dashevskiy, M., & Luo, Z. (2009). Reliable Probabilistic Classification of Internet Traffic. International Journal of Information Acquisition, 6(2), 133-146. https://doi.org/10.1142/S0219878909001837

Vancouver

Dashevskiy M, Luo Z. Reliable Probabilistic Classification of Internet Traffic. International Journal of Information Acquisition. 2009;6(2):133-146. https://doi.org/10.1142/S0219878909001837

Author

Dashevskiy, Mikhail ; Luo, Zhiyuan. / Reliable Probabilistic Classification of Internet Traffic. In: International Journal of Information Acquisition. 2009 ; Vol. 6, No. 2. pp. 133-146.

BibTeX

@article{d3d0cfe6874248969a70e04dfb7bda12,
title = "Reliable Probabilistic Classification of Internet Traffic",
abstract = "Classification of Internet traffic is very important to many applications such as network resource management, network security enforcement and intrusion detection. Many machine-learning algorithms have been successfully used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real Internet traffic datasets show the algorithmic framework works well. Comparison is also made to the published results.",
author = "Mikhail Dashevskiy and Zhiyuan Luo",
year = "2009",
doi = "10.1142/S0219878909001837",
language = "English",
volume = "6",
pages = "133--146",
journal = "International Journal of Information Acquisition",
issn = "0219-8789",
number = "2",

}

RIS

TY - JOUR

T1 - Reliable Probabilistic Classification of Internet Traffic

AU - Dashevskiy, Mikhail

AU - Luo, Zhiyuan

PY - 2009

Y1 - 2009

N2 - Classification of Internet traffic is very important to many applications such as network resource management, network security enforcement and intrusion detection. Many machine-learning algorithms have been successfully used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real Internet traffic datasets show the algorithmic framework works well. Comparison is also made to the published results.

AB - Classification of Internet traffic is very important to many applications such as network resource management, network security enforcement and intrusion detection. Many machine-learning algorithms have been successfully used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real Internet traffic datasets show the algorithmic framework works well. Comparison is also made to the published results.

U2 - 10.1142/S0219878909001837

DO - 10.1142/S0219878909001837

M3 - Article

VL - 6

SP - 133

EP - 146

JO - International Journal of Information Acquisition

JF - International Journal of Information Acquisition

SN - 0219-8789

IS - 2

ER -