Profiling Users by Modeling Web Transactions

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Abstract

Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given device. In this paper, we introduce a technique to profile users based on their web transactions. We compute several features extracted from a sequence of web transactions and use them with one-class classification techniques to profile a user. We assess the efficacy and speed of our method at differentiating 25 synthetic users on a benchmark dataset (from a major security vendor) representing 6 months of web traffic monitoring from a small enterprise network.

Details

Original languageEnglish
Title of host publication2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)
EditorsKisung Lee, Ling Liu
Publication statusPublished - 1 Jun 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Distributed Computing Systems - Atlanta, United States
Duration: 5 Jun 20178 Jun 2017
Conference number: 37

Publication series

NameInternational Conference on Distributed Computing Systems. Proceedings
PublisherIEEE Computer Society
Volume37
ISSN (Print)1063-6927

Conference

ConferenceInternational Conference on Distributed Computing Systems
Abbreviated titleICDCS
CountryUnited States
CityAtlanta
Period05/06/201708/06/2017

    Research areas

  • Internet, behavioural sciences computing, feature extraction, pattern classification, Web traffic monitoring, Web transaction modeling, benchmark dataset, characteristic behaviors, electronic devices, laptop, one-class classification techniques, small enterprise network, smartphone, user profiling, Feature extraction, Kernel, Media, Monitoring, Support vector machines, Uniform resource locators

ID: 15241443