Mini-Batch Ensemble Method on Keystroke Dynamics based User Authentication

Jiacang Ho, Dae-Ki Kang
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引用次数: 0

Abstract

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.
基于击键动力学的用户认证小批量集成方法
互联网使信息可以随时随地轻松流动。不幸的是,网络也成为犯罪分子进行身份盗窃等网络犯罪的重要工具。为了防止这个问题,使用一个非常复杂的密码并不是一个很好的方法。另外,击键动力学可以帮助用户解决问题。击键动力学是用户按下或释放键时的计时细节信息。通过适当的机器学习算法,机器可以从信息中学习用户的打字行为。在本文中,我们提出了一种小批量集成(MIBE)方法,该方法在原始数据集上进行预处理,最后产生多个小批量。然后通过机器学习算法训练小批量。从实验结果来看,每个基本算法的性能都有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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