An accurate evaluation of machine learning algorithms for flow-based P2P traffic detection

Soysal, Murat; Güran Schmidt, Ece

doi:10.1109/iscis.2007.4456894

Public

An accurate evaluation of machine learning algorithms for flow-based P2P traffic detection

Published • Nov 1, 2007

Authors:

Murat Soysal

Ece Güran Schmidt

Abstract

Today, peer-to-peer (P2P) traffic consumes the largest fraction of network bandwidth. The files shared by P2P communications are mostly copyright protected, and there are issues related to Quality of Service (QoS) support and billing of P2P traffic. Hence, scalable and accurate detection of peer-to-...

View

Paywalled

View Full Text

Subject

Computer science

Traffic classification

Scalability

Generate AI Take for this paper

Highlights, strengths & weaknesses, commercial applications, and societal impact — written for this paper on demand.

Subject

Generate AI Take for this paper

Discussions