Browse

Account-Sharing Detection Through Keystroke Dynamics Analysis

Cited 4 time in Web of Science Cited 6 time in Scopus
Authors
Hwang, Seong-seob; Lee, Hyoung-joo; Cho, Sungzoon
Issue Date
2009
Publisher
M E SHARPE INC
Citation
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE; Vol.14 2; 109-125
Keywords
Account sharingWeb site account managementtyping patternbiometricskeystroke dynamicsclustering
Abstract
Account sharing refers to a situation where multiple individuals share a Web site account to avoid paying a fee or providing personal information. As a result of account sharing, service providers lose revenue, underestimate membership, and have impaired understanding of their customers. A generic framework for detecting account sharing is proposed, using keystroke dynamics. Starting with the observation that a user''''''''s keystroke patterns are consistent and distinct from those of other individuals, it is assumed that each user''''''''s keystroke patterns form a "cluster" in Euclidean space. The number of sharers can be estimated by the number of clusters. In this paper, the "optimal" number of clusters is estimated based on the Bayesian model-selection framework with Gaussian mixture models obtained using the variational Bayesian approach. In a case study involving 25 passwords and 16 users, the proposed approach performed well in "sharing detection," with a 2 percent false alarm rate, a 2 percent miss rate, and a "total user estimation" error of 7 percent. The proposed approach is viable and merits further investigation.
ISSN
1086-4415
Language
English
URI
https://hdl.handle.net/10371/75355
DOI
https://doi.org/10.2753/JEC1086-4415140204
Files in This Item:
There are no files associated with this item.
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse