Effect of dimensionality reduction on performance in artificial neural network for user authentication
| Autor: | Sucheta Chauhan, K. V. Prema |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Password
Computer science business.industry Dimensionality reduction Probabilistic logic Keystroke logging computer.software_genre Machine learning ComputingMethodologies_PATTERNRECOGNITION Keystroke dynamics Dimensional reduction Principal component analysis Artificial intelligence Data mining Multidimensional scaling business computer |
| Zdroj: | 2013 3rd IEEE International Advance Computing Conference (IACC). |
| Popis: | Security is an important concern for today's generation, where keystroke-scan had come out as a milestone. In this paper, a comparison approach is presented for user authentication using keystroke dynamics. Here we have shown the effect of Dimensionality Reduction techniques on the performance and the misclassification rate is between 9.17% and 9.53%. It helps in improving the performance of the system after reducing the dimensions of input data. We have used three dimensional reduction techniques like: Principal Component Analysis (PCA), Multidimensional scaling (MDS), and probabilistic PCA. Here, PCA provide 9.17% misclassification rate with better performance for keystroke samples of 10 users and each user is having 400 samples for the same password. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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