Smartphones have now become ubiquitous for accessing and controlling home appliances in smart homes, a popular application of the Internet of Things. User authentication on smartphones is mostly achieved at initial access. However, without applying a continuous authentication process, the network will be susceptible to unauthorized users. This issue emphasizes the importance of offering a continuous authentication scheme to identify the current user of the device. This can be achieved by extracting information during smartphone usage, including application access patterns. In this paper, we present a flexible machine learning user authentication scheme for smart home networks based on smartphone usage. Considering that users may run their smartphone applications differently during different day time intervals as well as different days of the week, new features are extracted by considering this information. The scheme is evaluated on a real-world dataset for continuous user authentication. The results show that the presented scheme authenticates users with high accuracy.

中文翻译：

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使用应用程序交互和机器学习的智能家庭网络的灵活身份验证方案

智能手机现在已经普及到智能家居中的家用电器中，这是物联网的一种流行应用。智能手机上的用户身份验证主要是在初始访问时实现的。但是，如果不应用连续的身份验证过程，网络将容易受到未授权用户的攻击。此问题强调提供连续身份验证方案以标识设备的当前用户的重要性。这可以通过在智能手机使用期间提取信息（包括应用程序访问模式）来实现。在本文中，我们提出了一种基于智能手机使用情况的智能家庭网络灵活的机器学习用户身份验证方案。考虑到用户在不同的日期时间间隔和一周的不同日期可能会不同地运行其智能手机应用程序，通过考虑此信息来提取新功能。该方案在真实数据集上进行评估，以进行连续的用户身份验证。结果表明，所提出的方案对用户进行了高精度认证。