Smart homes have become increasingly popular for IoT products and services with a lot of promises for improving the quality of life of individuals. Nevertheless, the heterogeneous, dynamic, and Internet-connected nature of this environment adds new concerns as private data becomes accessible, often without the householders’ awareness. This accessibility alongside with the rising risks of data security and privacy breaches, makes smart home security a critical topic that deserves scrutiny. In this paper, we present an overview of the privacy and security challenges directed towards the smart home domain. We also identify constraints, evaluate solutions, and discuss a number of challenges and research issues where further investigation is required.

Smart connected home systems aim to enhance the comfort, convenience, security, entertainment, and health of the householders and their guests. Despite their advantages, their interconnected characteristics make smart home devices and services prone to various cybersecurity and privacy threats. In this paper, we analyze six classes of malicious threat agents for smart connected homes. We also identify four different motives and three distinct capability levels that can be used to group the different intruders. Based on this, we propose a new threat model that can be used for threat profiling. Both hypothetical and real-life examples of attacks are used throughout the paper. In reflecting on this work, we also observe motivations and agents that are not covered in standard agent taxonomies.

The Internet of Things is enabling innovative ser-vices promising added convenience and value in various domains such as the smart home. Increasingly, households, office envi-ronments and cities, are being fitted with smart camera systems aimed to enhance the security of citizens. At the same time, sev-eral systems being deployed suffer from weak security implemen-tations. Recognizing this, and to understand the extent of this situation, in this study we perform a global vulnerability assess-ment using the Shodan search engine and the Common Vulnera-bilities and Exposures database. This is done to detect smart con-nected cameras exposed on the Internet alongside their sensitive, potentially private, data being broadcasted. Furthermore, we discuss whether the discovered data can be used to compromise the safety and privacy of individuals, and identify some mitiga-tions that can be adopted. The results indicate that a significant number of smart cameras are indeed prone to diverse security and privacy vulnerabilities.

The home environment is rapidly becoming more complex with the introduction of numerous and heterogeneous Internet of Things devices. This development into smart connected homes brings with it challenges when it comes to gaining a deeper understanding of the home environment as a socio-technical system. A better understanding of the home is essential to build robust, resilient, and secure smart home systems. In this regard, we developed a novel method for classifying smart home devices in a logical and coherent manner according to their functionality. Unlike other approaches, we build the categorization empirically by mining the technical specifications of 1,193 commercial devices. Moreover, we identify twelve capabilities that can be used to characterize home devices. Alongside the classification, we also quantitatively analyze the entire spectrum of commercial smart home devices in accordance to their functionality and capabilities. Overall, the categorization and analysis provide a foundation for identifying opportunities of generalizations and common solutions for the smart home.

The increasing presence of heterogeneous Internet of Things devices inside the home brings with it added convenience and value to the householders. At the same time, these devices tend to be Internet-connected and continuously monitor and collect data about the residents and their daily lifestyle activities. Such data can be of a sensitive nature, given that the house is the place where privacy is naturally expected. To gain insight into this state of affairs, we empirically investigate the privacy policies of 87 different categories of commercial smart home devices in terms of data being collected. This is done using a combination of manual and data mining techniques. The overall contribution of this work is a model that identifies and categorizes smart connected home data in terms of its collection mode, collection method, and collection phase. Our findings bring up several implications for smart connected home privacy, which include the need for better security controls to safeguard the privacy of the householders.