The Internet of Things is a rapidly growing paradigm for smart cities that provides a way of communication, identification, and sensing capabilities among physically distributed devices. With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased. This demands secure authentication mechanisms to preserve the users’ privacy when interacting with smart devices. This paper proposes a heterogeneous framework “ADLAuth” for passive and implicit authentication of the user using either a smartphone’s built-in sensor or wearable sensors by analyzing the physical activity patterns of the users. Multiclass machine learning algorithms are applied to users’ identity verification. Analyses are performed on three different datasets of heterogeneous sensors for a diverse number of activities. A series of experiments have been performed to test the effectiveness of the proposed framework. The results demonstrate the better performance of the proposed scheme compared to existing work for user authentication.

In recent years, with the continuous evolvement in Artificial Intelligence (AI) and Information and Communication Technologies (ICTs), including Internet-of-Things (IoT) and cloud computing (CC), computers are anticipated to replace human beings in almost all fields of life. Smartphones and other handheld devices have evolved from simple communication devices to personal computers. They have gained popularity due to their convenient use in everyday life for accessing various online services, social networks, and e-banking, etc. People use smartphones for not only personal use but also take advantage of these devices in their business-related tasks. Consequently, increasing amounts of private and sensitive information are being generated and stored in our

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