Effect of dimensionality reduction on performance in artificial neural network for user authentication

Sucheta Chauhan, K. V. Prema

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013
Pages788-793
Number of pages6
DOIs
Publication statusPublished - 12-07-2013
Event2013 3rd IEEE International Advance Computing Conference, IACC 2013 - Ghaziabad, India
Duration: 22-02-201323-02-2013

Conference

Conference2013 3rd IEEE International Advance Computing Conference, IACC 2013
CountryIndia
CityGhaziabad
Period22-02-1323-02-13

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Chauhan, S., & Prema, K. V. (2013). Effect of dimensionality reduction on performance in artificial neural network for user authentication. In Proceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013 (pp. 788-793). [6514327] https://doi.org/10.1109/IAdCC.2013.6514327