Publication:
Analysis and classification of web proxy logs based on patterns of traffic rates

dc.contributor.authorNattapol Kiatkumjounwongen_US
dc.contributor.authorSudsanguan Ngamsuriyarojen_US
dc.contributor.authorAnon Plangprasopchoken_US
dc.contributor.authorApirak Hoonloren_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherThailand National Electronics and Computer Technology Centeren_US
dc.date.accessioned2018-11-23T10:02:13Z
dc.date.available2018-11-23T10:02:13Z
dc.date.issued2015-01-01en_US
dc.description.abstract© 2014 IEEE. Logs are typically used for performing post mortem for abnormal activities. Most Internet service providers keep the history of users' web accesses in terms of proxy logs for investigating a misuse or fraud. However, the majority of the logs represent normal behavior, and no thorough analysis of such logs is usually performed, keeping them on storage would consume very big space. This paper analyzes the characteristics of such logs and classifies them into normal, medium, high and burst rate using five main attributes: IP address, bandwidth, duration, file category, and file type. Our experimental results show different rates for each file type in five popular file categories. The results will be used in classifying web access logs and filtering out abnormal from normal logs so that only abnormal logs are kept for fast investigation.en_US
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol.2015-January, (2015)en_US
dc.identifier.doi10.1109/TENCON.2014.7022457en_US
dc.identifier.issn21593450en_US
dc.identifier.issn21593442en_US
dc.identifier.other2-s2.0-84940514925en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/35839
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84940514925&origin=inwarden_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.titleAnalysis and classification of web proxy logs based on patterns of traffic ratesen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84940514925&origin=inwarden_US

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