Earticle

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

Abstract
 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

키워드

저자

• Jiacang Ho [ Department of Ubiquitous IT, Graduate School, Dongseo University ]
• Dae-Ki Kang [ Department of Computer & Information Engineering, Dongseo University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380