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eHealth Traffic Detection and Classification Using Machine Learning Techniques

Authors:
Monika Grajzer
Piotr Szczechowiak

Keywords: eHealth applications, traffic classification, flow analysis, machine learning

Abstract:
With the growing number of available eHealth applications, the amount of eHealth traffic transmitted through communication networks increases significantly. This implies that network mechanisms must provide Quality of Service (QoS) assurances to support these new applications. In order to improve network performance, there is a need to develop new QoS methods that would properly detect and classify eHealth traffic. In this paper we present a selection of machine learning - based traffic classification methods in the context of eHealth services provisioning. We also present a mapping of eHealth application classes to appropriate QoS classes. Finally we propose an eHealth-aware approach, which can perform real-time traffic classification. In this technique the packet content is not inspected and at the same time the privacy of transmitted information is preserved.

Pages: 151 to 154

Copyright: Copyright (c) IARIA, 2012

Publication date: January 30, 2012

Published in: conference

ISSN: 2308-4359

ISBN: 978-1-61208-179-3

Location: Valencia, Spain

Dates: from January 30, 2012 to February 4, 2012