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首页> 外文会议>International Carnahan Conference on Security Technology >Supervised classification methods applied to keystroke dynamics through mobile devices
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Supervised classification methods applied to keystroke dynamics through mobile devices

机译：通过移动设备应用于按键动态的监督分类方法

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Keystroke dynamics biometrics through computers are based in the time that users need to press and hold keys and often present too small amount of information. This limitation is eliminated in the environment of mobile devices due to a variety of sensors (accelerometers, gyroscopes, pressure and finger size) can be used to acquire useful information from users. These data have been acquired within the scenario of typing a 4-digit PIN in order to analyze the possibilites of reinforcing the security of mobile devices. A database with keystroke dynamics patterns has been analysed. Data has been acquired in a constrained environment, where users must hold the phone in a fixed position, and other with the data taken in unconstrained conditions. Features as pressure, finger size, times, linear an angular acceleration are extracted and processed. Supervised classification methods are widely used in different kind of biometrics. A discussion about their use in keystroke biometrics is presented. A preprocessing of the acquired data is performed using Linear Discriminant Analysis (LDA) and a reduction of the amount of information applying Principal Components Analysis (PCA). This preprocessing enhances considerably the results obtained in classification. We conclude claiming that biometric systems through keystroke dynamics with 4-digit PIN are promising.
机译：通过计算机进行的按键动力学生物特征识别基于用户需要按下和按住键的时间，并且经常显示的信息量太少。由于可以使用各种传感器（加速度计，陀螺仪，压力和手指大小）来从用户那里获取有用的信息，因此在移动设备的环境中消除了此限制。这些数据是在键入4位PIN的情况下获取的，以便分析增强移动设备安全性的可能性。分析了具有击键动态模式的数据库。数据是在受限的环境中获取的，在该环境中，用户必须将手机固定在一个固定的位置，而其他数据则是在不受约束的条件下获取的。提取并处理压力，手指大小，时间，线性角加速度等特征。监督分类方法广泛用于不同种类的生物特征识别中。讨论了它们在按键生物识别技术中的使用。使用线性判别分析（LDA）对获取的数据进行预处理，并应用主成分分析（PCA）减少信息量。这种预处理大大提高了分类中获得的结果。我们得出结论，声称通过具有4位PIN的击键动力学的生物识别系统很有希望。

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