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Security monitoring through human computer interaction devices.

机译：通过人机交互设备进行安全监控。

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摘要

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.
机译：在这项工作中，我们介绍了一种基于鼠标动力学的行为生物识别的新形式，可以在不同的安全应用程序中使用。我们开发了一种技术，可用于使用人工神经网络从捕获的数据建模行为特征。此外，我们介绍了检测器的体系结构和实现，其中涵盖了生物识别数据流的所有阶段，包括检测过程。我们还介绍了一种用于击键生物特征分析的新技术，该技术支持自由文本检测，从而可以对用户进行被动，动态和实时监控。也可以被动地完成注册过程，而无需用户输入特定的文本。提供并分析了说明为评估所提出的检测技术的准确性而进行的实验的实验数据。我们将研究进一步向前，针对连续认证（CA）的一般领域。 CA系统通过在整个登录会话中动态重复几次身份验证过程，从而摆脱了传统的（静态）身份验证方案。主要目标是检测伪装者，确保会话安全并与内部威胁进行斗争。鼠标和击键动力学是CA的理想选择。 CA是一个新兴领域，我们相信它将在未来许多组织的整体安全策略中扮演重要角色。因此，随着技术的成熟和日趋多样化，开发可用于比较和对比现有方案与未来方案的通用且有意义的评估指标至关重要。到目前为止，文献中提出的所有CA系统都已使用与静态身份验证系统相同的准确性度量进行了评估，并且在某些情况下，还使用了警报时间（TTA）度量的简化形式。作为替代方案，我们在这项工作中提出了动态准确性指标，可以更好地反映CA活动的连续性。此外，我们介绍并研究了多种形式的更复杂的警报时间（TTA）指标。我们使用综合的和真实的数据样本结合经验地研究和说明所提出的度量和模型。

著录项

  • 作者

    Ahmed, Ahmed Awad El Sayed.;

  • 作者单位

    University of Victoria (Canada).;

  • 授予单位 University of Victoria (Canada).;
  • 学科 Computer science.;Information science.;Artificial intelligence.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 167 p.
  • 总页数 167
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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