User Authentication from Mouse Movement Data Using SVM Classifier
| Autor: | Bashira Akter Anima, Mahmood Jasim, Hasanuzzaman, Khandaker Abir Rahman, Adam Rulapaugh |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
021110 strategic
defence & security studies User authentication Biometrics Computer science business.industry Feature vector 05 social sciences 0211 other engineering and technologies Pattern recognition 02 engineering and technology computer.software_genre Mouse button 050105 experimental psychology law.invention Support vector machine law Principal component analysis 0501 psychology and cognitive sciences Data mining Artificial intelligence business Classifier (UML) computer Block size |
| Zdroj: | Cryptology and Network Security ISBN: 9783319489643 CANS |
| DOI: | 10.1007/978-3-319-48965-0_47 |
| Popis: | This paper presents a robust user authentication system by gleaning raw mouse movement data. The data was collected using a publicly available tool called Recording User Input (RUI) from 23 subjects analyzed for three types of mouse actions - Mouse Move, Point-and-Click on Left or Right mouse button, and Drag-and-Drop. Samples are broken down to unit blocks comprising a certain number of actions and from each block seventy-four features are extracted to construct feature vectors. The proposed system was rigorously tested against public benchmark data. Experiment results generated by using the Support Vector Machine (SVM) classifier shows a False Rejection Rate (FRR) of 1.1594 % and a False Acceptance Rate (FAR) of 1.9053 % when the block size was set for 600 actions. After reducing dimensions using Principle Component Analysis (PCA), SVM classifier shows FRR of 1.2081 % and FAR of 2.3604 %. Compared with the existing methods based on mouse movements, our method shows significantly lower error rates, which we opine are viable enough to become an alternate to conventional authentication systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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