Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks

This study proposes a capable, scalable, and reliable edge-to-edge model for filtering malicious traffic through real-time monitoring of the impact of user behavior on quality of service (QoS) regulations. The model investigates user traffic, including that injected through distributed gateways and...

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Main Authors:	Ahmed, Abdulghani Ali, Aman, Jantan, Wan, Tat-Chee
Format:	Article
Language:	English
Published:	Elsevier 2015
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/12716/ http://umpir.ump.edu.my/id/eprint/12716/ http://umpir.ump.edu.my/id/eprint/12716/ http://umpir.ump.edu.my/id/eprint/12716/1/Filtration%20Model%20for%20the%20Detection%20Of%20Malicious%20Traffic%20In%20Large-Scale%20Networks.pdf

id	ump-12716
recordtype	eprints
spelling	ump-127162018-05-16T07:53:20Z http://umpir.ump.edu.my/id/eprint/12716/ Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks Ahmed, Abdulghani Ali Aman, Jantan Wan, Tat-Chee QA75 Electronic computers. Computer science This study proposes a capable, scalable, and reliable edge-to-edge model for filtering malicious traffic through real-time monitoring of the impact of user behavior on quality of service (QoS) regulations. The model investigates user traffic, including that injected through distributed gateways and that destined to gateways that are experiencing actual attacks. Misbehaving traffic filtration is triggered only when the network is congested, at which point burst gateways generate an explicit congestion notification (ECN) to misbehaving users. To investigate the behavior of misbehaving user traffic, packet delay variation (PDV) ratios are actively estimated and packet transfer rates are passively measured at a unit time. Users who exceed the PDV bit rates specified in their service level agreements (SLAs) are filtered as suspicious users. In addition, suspicious users who exceed the SLA bandwidth bit rates are filtered as network intruders. Simulation results demonstrate that the proposed model efficiently filters network traffic and precisely detects malicious traffic Elsevier 2015 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/12716/1/Filtration%20Model%20for%20the%20Detection%20Of%20Malicious%20Traffic%20In%20Large-Scale%20Networks.pdf Ahmed, Abdulghani Ali and Aman, Jantan and Wan, Tat-Chee (2015) Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks. Computer Communications. ISSN 0140-3664 (In Press) http://dx.doi.org/10.1016/j.comcom.2015.10.012 DOI: 10.1016/j.comcom.2015.10.012
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Ahmed, Abdulghani Ali Aman, Jantan Wan, Tat-Chee Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks
description	This study proposes a capable, scalable, and reliable edge-to-edge model for filtering malicious traffic through real-time monitoring of the impact of user behavior on quality of service (QoS) regulations. The model investigates user traffic, including that injected through distributed gateways and that destined to gateways that are experiencing actual attacks. Misbehaving traffic filtration is triggered only when the network is congested, at which point burst gateways generate an explicit congestion notification (ECN) to misbehaving users. To investigate the behavior of misbehaving user traffic, packet delay variation (PDV) ratios are actively estimated and packet transfer rates are passively measured at a unit time. Users who exceed the PDV bit rates specified in their service level agreements (SLAs) are filtered as suspicious users. In addition, suspicious users who exceed the SLA bandwidth bit rates are filtered as network intruders. Simulation results demonstrate that the proposed model efficiently filters network traffic and precisely detects malicious traffic
format	Article
author	Ahmed, Abdulghani Ali Aman, Jantan Wan, Tat-Chee
author_facet	Ahmed, Abdulghani Ali Aman, Jantan Wan, Tat-Chee
author_sort	Ahmed, Abdulghani Ali
title	Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks
title_short	Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks
title_full	Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks
title_fullStr	Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks
title_full_unstemmed	Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks
title_sort	filtration model for the detection of malicious traffic in large-scale networks
publisher	Elsevier
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/12716/ http://umpir.ump.edu.my/id/eprint/12716/ http://umpir.ump.edu.my/id/eprint/12716/ http://umpir.ump.edu.my/id/eprint/12716/1/Filtration%20Model%20for%20the%20Detection%20Of%20Malicious%20Traffic%20In%20Large-Scale%20Networks.pdf
first_indexed	2023-09-18T22:14:35Z
last_indexed	2023-09-18T22:14:35Z
_version_	1777415252122533888

Filtration Model for the Detection of Malicious Traffic in Large-Scale Networks

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