Security monitoring through human computer interaction devices
Date
2010-06-14T18:49:23Z
Authors
Ahmed, Ahmed Awad El Sayed
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Abstract
In this work we introduce a new form of behavioral biometrics based on mouse dynamics, which can be used in different security applications. We develop a technique that can be used to model the behavioral characteristics from the captured data using artificial neural networks. In addition. we present an architecture and implementation for the detector, which cover all the phases of the biometric data flow including the detection process. We also introduce a new technique for keystroke biometrics analysis which supports free text detection allowing passive, dynamic, and real-time monitoring of users. The enrollment process can also be done passively without requiring the user to enter a specific text. Experimental data illustrating the experiments conducted to evaluate the accuracy of the proposed detection techniques are presented and analyzed. We take the study a step further and target the general field of Continuous Authentication (CA). CA systems depart from traditional (static) authentication scheme by repeating several times the authentication process dynamically throughout the entire login session. The main objectives being to detect masqueraders, ensure session security, and combat insider threat. Mouse and Keystroke dynamics are good candidates for CA. CA is an emerging field that we believe will play an important role in the overall security strategies of many organizations in the future. Thus, as the technology gains in maturity and becomes more diverse, it is essential to develop common and meaningful evaluation metrics that can be used to compare and contrast between existing and future schemes. So far, all the CA systems proposed in the literature have been evaluated using the same accuracy metrics used for static authentication systems and, in some cases, using a simplified form of the Time-To-Alarm (TTA) metric. As an alternative, we propose in this work dynamic accuracy metrics that better capture the continuous nature of CA activity. Furthermore, we introduce and study diverse and more complex forms of the Time-to-Alarm (TTA) metrics. We study and illustrate empirically the proposed metrics and models using a combination of synthetic and real data samples.
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Keywords
Biometrics, Detection