# Threat Analysis for Smart Homes +
## 1 Introduction  
Dynamic environments are characterized by changes in their architecture, data flows, and operational processes. Such changes can appear periodically, continuously, or be event triggered. The Internet of Things (IoT) is a typical example of such an environment, with instantiations across various ecosystems such as smart homes, eHealth, vehicular networks, cloud computing and mobile communications. This dynamicity, along with the increased interconnectivity and enhanced operational features increase the attack surface of such ecosystems [1]. Smart homes is a typical instantiation of a dynamic ecosystem where the ICT penetration is significant since several types of connected devices and locally or remotely deployed services leverage ICT. Many works in the literature have introduced definitions for the \"smart home\" [2; 3; 4]. Smart homes can be defined by taking a social perspective or a technical viewpoint. The former describes the influence of the smart home to human and social needs, whilst the latter describes the systems, processes, services, and smart devices which are connected so as to facilitate control over the home's ecosystem. A smart home is able to support diversified components and entities, such as utility suppliers, infrastructure providers and third party software or hardware vendors [5]. Due to this diversity, the attack surface of the smart home is increasing rapidly, since more security vulnerabilities are introduced, paving the way to an unreliable and insecure ecosystem. To this end, the EuropeanUnion Agency for Cybersecurity (ENISA) [6] identified potential threats and proposed good practices for their mitigation. Existing risk assessment methods are mostly suitable for static environments and systems. Dynamic environments such as those described above, require risk assessment methods able to cope with frequent changes in the environment. The scope of this work is to carry out a threat analysis for the smart home ecosystem, as the first step towards a comprehensive dynamic risk assessment method, by leveraging an existing smart home reference architecture. Furthermore, by considering the technical viewpoint that defines the smart home environment, we design a smart home network topology generator, based on the existing reference architecture. Subsequently, to provide knowledge about the effects of the dynamic dependencies of the network on the propagation of a malware infection, we use the outputs provided by the topology generator to create a graph-based attack model. Our attack model is based on the initial hypothesis that at any given time \(t\) each node can be observed at a discrete state and. subsequently, after homogeneous intervals of time \(\Delta t\), it can either transit to another state or remain at the current state. The model assumes types of malware that use either random or localized scanning techniques. A malware that employs random scanning selects target IP addresses at random [7]. Localized scanning malware preferentially scans for hosts in the \"local\" address space, instead of selecting targets randomly [7]. The contributions of this paper can be summarized as follows: (i) a review of dynamic risk assessment methodologies appropriate for identifying and assessing risks within the smart home ecosystem; (ii) a threat analysis on an existing smart home reference architecture focusing on the data flows and cloud services; (iii) an algorithm for modeling and visualizing the dynamic changes in a smart home topology; and (iv) a study of the propagation of a malware infection in such networks, with the use of connected graphs. The remainder of this paper is structured as follows: Section 2 reviews related work. In Section 3, we describe the reference architecture of the smart home.