WiFi-Enabled User Authentication through Deep Learning in Daily Activities
WiFi-Enabled User Authentication through Deep Learning in Daily Activities
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DOI：
10.1145/3448738
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发表时间：
2021-05
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影响因子：
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通讯作者：
Cong Shi;Jian Liu;Hongbo Liu;Yingying Chen
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文献类型：
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作者：
Cong Shi;Jian Liu;Hongbo Liu;Yingying Chen
由于日益增长的安全和隐私问题，用户身份验证在企业和家庭环境中都是一个关键过程。随着智能城市和家庭环境的发展，用户身份验证的概念不仅可以防止未经授权的用户访问机密信息，而且还可以为特定用户提供定制服务的机会。传统的用户认证方法要么需要专门的设备安装，要么需要不方便的可穿戴传感器附件。本文通过利用物联网设备（如智能冰箱、智能电视和智能恒温器等）提供的WiFi信号，支持无设备用户认证的扩展概念，该系统利用WiFi信号捕获从日常活动（包括步行和静止）中继承的独特的人类生理和行为特征。特别是，我们从WiFi信号的信道状态信息（CSI）测量中提取代表性特征，并开发了一种基于深度学习的用户认证方案，以准确识别每个用户。为了减轻周围人的运动引起的信号失真，我们的深度学习模型利用了基于CNN的架构，该架构建设性地结合了来自多个接收天线的特征，并获得了更可靠的特征抽象。此外，开发了一种基于迁移学习的机制，以减少新用户和环境的培训成本。在各种室内环境中进行了广泛的实验，以证明所提出的认证系统的有效性。特别是，我们的系统可以通过不同的活动实现11个主题的认证准确率超过94%。
User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This article supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV, and smart thermostat, and so on. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep-learning-based user authentication scheme to accurately identify each individual user. To mitigate the signal distortion caused by surrounding people’s movements, our deep learning model exploits a CNN-based architecture that constructively combines features from multiple receiving antennas and derives more reliable feature abstractions. Furthermore, a transfer-learning-based mechanism is developed to reduce the training cost for new users and environments. Extensive experiments in various indoor environments are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% authentication accuracy with 11 subjects through different activities.