원문정보
초록
영어
The internet allows the information to flow at anywhere in anytime easily. Unfortunately, the network also becomes a great tool for the criminals to operate cybercrimes such as identity theft. To prevent the issue, using a very complex password is not a very encouraging method. Alternatively, keystroke dynamics helps the user to solve the problem. Keystroke dynamics is the information of timing details when a user presses a key or releases a key. A machine can learn a user typing behavior from the information integrate with a proper machine learning algorithm. In this paper, we have proposed mini-batch ensemble (MIBE) method which does the preprocessing on the original dataset and then produces multiple mini batches in the end. The mini batches are then trained by a machine learning algorithm. From the experimental result, we have shown the improvement of the performance for each base algorithm.
목차
1. Introduction
2. Mini-Batch Ensemble Method
2.1 Motivation
2.2 Distribution of mini-batch
2.3 Training phase and testing phase
3. Dataset
3.1 CMU benchmark dataset
3.2 GREYC web-based keystroke dynamics dataset
4. Experimental Result
5. Conclusion
6. Acknowledgement
References