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首页> 外文期刊>IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics >A parallel decision tree-based method for user authentication based on keystroke patterns
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A parallel decision tree-based method for user authentication based on keystroke patterns

机译：基于击键模式的基于并行决策树的用户认证方法

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摘要

We propose a Monte Carlo approach to attain sufficient training data, a splitting method to improve effectiveness, and a system composed of parallel decision trees (DTs) to authenticate users based on keystroke patterns. For each user, approximately 19 times as much simulated data was generated to complement the 387 vectors of raw data. The training set, including raw and simulated data, is split into four subsets. For each subset, wavelet transforms are performed to obtain a total of eight training subsets for each user. Eight DTs are thus trained using the eight subsets. A parallel DT is constructed for each user, which contains all eight DTs with a criterion for its output that it authenticates the user if at least three DTs do so; otherwise it rejects the user. Training and testing data were collected from 43 users who typed the exact same string of length 37 nine consecutive times to provide data for training purposes. The users typed the same string at various times over a period from November through December 2002 to provide test data. The average false reject rate was 9.62% and the average false accept rate was 0.88%.
机译：我们提出了一种蒙特卡罗方法来获得足够的训练数据，一种提高效率的分裂方法，以及一种由并行决策树（DT）组成的系统，用于根据击键模式对用户进行身份验证。对于每个用户，生成的模拟数据大约是19倍，以补充原始数据的387个向量。训练集（包括原始数据和模拟数据）被分为四个子集。对于每个子集，执行小波变换以获得每个用户的总共八个训练子集。因此，使用八个子集训练八个DT。为每个用户构造一个并行DT，其中包含所有八个DT，并带有一个输出标准，如果至少有三个DT这样做，它将验证用户的身份。否则，它将拒绝用户。收集了43位用户的培训和测试数据，他们连续9次键入了完全相同的长度为37的字符串，以提供用于培训目的的数据。用户在2002年11月至2002年12月的不同时间键入相同的字符串以提供测试数据。平均错误拒绝率为9.62％，平均错误接受率为0.88％。

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