Show simple item record

dc.contributor.authorShaman, Faisal
dc.contributor.authorGhita, B
dc.contributor.authorClarke, Nathan
dc.contributor.authorAlruban, A
dc.date.accessioned2019-11-03T19:51:55Z
dc.date.available2019-11-03T19:51:55Z
dc.date.issued2019-06
dc.identifier.isbn9781728128276
dc.identifier.urihttp://hdl.handle.net/10026.1/15108
dc.description.abstract

There is an increasing interest to identify users and behaviour profiling from network traffic metadata for traffic engineering and security monitoring. Network security administrators and internet service providers need to create the user behaviour traffic profile to make an informed decision about policing, traffic management, and investigate the different network security perspectives. Additionally, the analysis of network traffic metadata and extraction of feature sets to understand trends in application usage can be significant in terms of identifying and profiling the user by representing the user's activity. However, user identification and behaviour profiling in real-time network management remains a challenge, as the behaviour and underline interaction of network applications are permanently changing. In parallel, user behaviour is also changing and adapting, as the online interaction environment changes. Also, the challenge is how to adequately describe the user activity among generic network traffic in terms of identifying the user and his changing behaviour over time. In this paper, we propose a novel mechanism for user identification and behaviour profiling and analysing individual usage per application. The research considered the application-level flow sessions identified based on Domain Name System filtering criteria and timing resolution bins (24-hour timing bins) leading to an extended set of features. Validation of the module was conducted by collecting NetFlow records for a 60 days from 23 users. A gradient boosting supervised machine learning algorithm was leveraged for modelling user identification based upon the selected features. The proposed method yields an accuracy for identifying a user based on the proposed features up to 74%

dc.format.extent1-8
dc.language.isoen
dc.publisherIEEE
dc.rightsAttribution-NonCommercial 4.0 International
dc.rightsAttribution-NonCommercial 4.0 International
dc.rightsAttribution-NonCommercial 4.0 International
dc.rightsAttribution-NonCommercial 4.0 International
dc.rightsAttribution-NonCommercial 4.0 International
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectuser profile
dc.subjectuser behavioural
dc.subjectuser identification
dc.subjectnetwork traffic analysis
dc.subjectsupervised learning
dc.subjectnetwork security
dc.titleUser Profiling Based on Application-Level Using Network Metadata
dc.typeconference
dc.typeConference Proceeding
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000490864900017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.date-start2019-06-10
plymouth.date-finish2019-06-12
plymouth.volume00
plymouth.publisher-urlhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8750993
plymouth.conference-name2019 7th International Symposium on Digital Forensics and Security (ISDFS)
plymouth.publication-statusPublished
plymouth.journal2019 7th International Symposium on Digital Forensics and Security (ISDFS)
dc.identifier.doi10.1109/isdfs.2019.8757503
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1109/isdfs.2019.8757503
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc/4.0/
rioxxterms.typeConference Paper/Proceeding/Abstract


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial 4.0 International

All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.
Theme by 
Atmire NV