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• W344510002 abstract "The need for other security measures besides secret keywords has highlightedthe possibility to base a biometric security system on input device patterns.This report explores the feasability of using mouse and keyboard biometry inan online security system. After examining prior work in this area, an onlinetest environment was set up where subjects could remotely complete experimentsessions by interacting with a web-site.Artificial Neural Networks (ANN), k-Nearest Neighbor (k-NN) and Self OrganizingMaps (SOM) were combined into profiles which predicted sample validity.A final system accuracy of 0.92% False Accept Rate (FAR) and 43.16% FalseReject Rate (FRR) was achieved. The best profile in the system achieved a FARof 0.8% and a FRR of 0.0%. When one of the 19 subjects was browsing the online test environment, the system could with an accuracy of 54.1% correctly identify the user. Examining the results, it is clear that data of a higher qualityis needed and alternative collection methods should be explored. It seems feasibleto recognize a registered user solely based on keyboard, mouse and browser variables." @default.
• W344510002 created "2016-06-24" @default.
• W344510002 creator A5019909070 @default.
• W344510002 creator A5040851541 @default.
• W344510002 date "2009-01-01" @default.
• W344510002 modified "2023-09-27" @default.
• W344510002 title "Identifying Users Based on Use of Input Devices" @default.
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• W344510002 hasPublicationYear "2009" @default.
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