Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

ORCID Identifiers

0000-0001-6279-9776

Document Type

Article

Source of Publication

Personal and Ubiquitous Computing

Publication Date

11-1-2019

Abstract

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy.

ISSN

1617-4909

Publisher

Springer London

Volume

23

Issue

5-6

First Page

777

Last Page

791

Disciplines

Electrical and Computer Engineering

Keywords

Decision trees, Encrypted traffic, Intrusion detection, Multicriterion fuzzy decision making, Network management and security, Network traffic classification

Scopus ID

85034067841

Indexed in Scopus

yes

Open Access

no

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