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Continuous authentication using biometrics: An advanced review
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2020-03-24 , DOI: 10.1002/widm.1365
Gabriel Dahia 1 , Leone Jesus 1 , Maurício Pamplona Segundo 1
Affiliation  

The shortcomings of conventional access control systems for high‐security environments have led to the concert of continuous authentication. Contrary to traditional verification, in which users are authenticated only once at the start of their session, continuous authentication systems regularly check users' identities to prevent hijackings. The challenges in this area involve balancing the security of protected assets by quickly detecting intruders with the system usability for genuine users. Biometric recognition plays a major role within this context, as it is the main way to assure that users are who they claim to be. A comparative analysis of the latest works revealed different aspects of this problem. First, some biometrics traits among those applied for continuous authentication are more suitable for this task than others. Second, systems combining multiple traits have advantages over those relying on a single one. Finally, many works fail to report proper evaluation metrics. With this in mind, we were able to identify new opportunities for researchers in the field. We highlight the potential for mining new datasets on the internet, which would benefit validation and benchmarking, and how recent deep learning techniques could address some of the open challenges in the area.

中文翻译:

使用生物识别技术进行连续身份验证:高级审查

常规访问控制系统在高安全性环境中的缺点导致了连续身份验证的协同作用。与传统的验证(在会话开始时仅对用户进行一次身份验证)相反,连续身份验证系统会定期检查用户的身份以防止劫持。该领域的挑战包括通过快速检测入侵者与真正用户的系统可用性来平衡受保护资产的安全性。生物特征识别在这种情况下起着重要作用,因为这是确保用户是他们声称的身份的主要方式。对最新作品的比较分析揭示了该问题的不同方面。首先,用于连续认证的生物特征中的一些比其他特征更适合此任务。第二,结合了多个特征的系统比那些依赖一个特征的系统具有优势。最后,许多作品未能报告适当的评估指标。考虑到这一点,我们能够为该领域的研究人员发现新的机会。我们强调了在互联网上挖掘新数据集的潜力,这将有益于验证和基准测试,以及最新的深度学习技术如何解决该领域的一些开放挑战。
更新日期:2020-03-24
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