B2auth:用于真实世界部署的上下文细粒度行为生物识别身份验证框架

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ahmed Mahfouz , Ahmed Hamdy , Mohamed Alaa Eldin , Tarek M. Mahmoud
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引用次数: 0

摘要

目前已提出了几种行为生物识别身份验证框架,根据对传感器和服务的分析对智能手机用户进行身份验证。这些认证框架通过提取一系列行为特征(如触摸、传感器和按键动态)来验证用户身份,并使用机器学习和深度学习技术来开发认证模型。遗憾的是,目前还不清楚这些框架在实际部署中的表现如何,文献中的大多数实验都是在受控环境中与合作用户进行的。在本文中,我们提出了一个新颖的行为生物识别身份验证框架,名为 B2auth,专门为智能手机用户设计。该框架利用从智能手机触摸屏收集到的原始数据提取行为特征,用于身份验证。该框架采用多层感知器(MLP)神经网络来开发身份验证模型。与许多在受控环境中与合作用户进行的现有实验不同,我们将重点放在真实世界的部署场景上,收集了 60 名在不受控环境中使用智能手机的参与者的数据。该框架在各种应用环境下区分合法所有者和攻击者方面取得了可喜的成果,展示了其在实际应用案例中的潜力。通过利用简约的数据收集和基于云的模型生成,B2auth 框架为智能手机的行为生物识别身份验证提供了一种高效、有效的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
B2auth: A contextual fine-grained behavioral biometric authentication framework for real-world deployment

Several behavioral biometric authentication frameworks have been proposed to authenticate smartphone users based on the analysis of sensors and services. These authentication frameworks verify the user identity by extracting a set of behavioral traits such as touch, sensors and keystroke dynamics, and use machine learning and deep learning techniques to develop the authentication models. Unfortunately, it is not clear how these frameworks perform in the real world deployment and most of the experiments in the literature have been conducted with cooperative users in a controlled environment. In this paper, we present a novel behavioral biometric authentication framework, called B2auth, designed specifically for smartphone users. The framework leverages raw data collected from touchscreen on smartphone to extract behavioral traits for authentication. A Multilayer Perceptron (MLP) neural network is employed to develop authentication models. Unlike many existing experiments conducted in controlled environments with cooperative users, we focused on real-world deployment scenarios, collecting data from 60 participants using smartphones in an uncontrolled environment. The framework achieves promising results in differentiating the legitimate owner and an attacker across various app contexts, showcasing its potential in practical use cases. By utilizing minimalist data collection and cloud-based model generation, the B2auth framework offers an efficient and effective approach to behavioral biometric authentication for smartphones.

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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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