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10.1007_s42454-020-00010-2.pdf (1.13 MB)

A heuristics for HTTP traffic identification in measuring user dissimilarity

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journal contribution
posted on 2022-11-22, 21:16 authored by Adeyemi R. Ikuesan, Mazleena Salleh, Hein S. Venter, Shukor Abd Razak, Steven M. Furnell

The prevalence of HTTP web traffic on the Internet has long transcended the layer 7 classification, to layers such as layer 5 of the OSI model stack. This coupled with the integration-diversity of other layers and application layer protocols has made identification of user-initiated HTTP web traffic complex, thus increasing user anonymity on the Internet. This study reveals that, with the current complex nature of Internet and HTTP traffic, browser complexity, dynamic web programming structure, the surge in network delay, and unstable user behavior in network interaction, user-initiated requests can be accurately determined. The study utilizes HTTP request method of GET filtering, to develop a heuristic algorithm to identify user-initiated requests. The algorithm was experimentally tested on a group of users, to ascertain the certainty of identifying user-initiated requests. The result demonstrates that user-initiated HTTP requests can be reliably identified with a recall rate at 0.94 and F-measure at 0.969. Additionally, this study extends the paradigm of user identification based on the intrinsic characteristics of users, exhibited in network traffic. The application of these research findings finds relevance in user identification for insider investigation, e-commerce, and e-learning system as well as in network planning and management. Further, the findings from the study are relevant in web usage mining, where user-initiated action comprises the fundamental unit of measurement.

Other Information

Published in: Human-Intelligent Systems Integration
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: http://dx.doi.org/10.1007/s42454-020-00010-2

History

Language

  • English

Publisher

Springer Science and Business Media LLC

Publication Year

  • 2020

Institution affiliated with

  • Community College of Qatar

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