# Threat Analysis for Smart Homes +
## 8 Conclusions  
To facilitate threat analysis for dynamic environments, it is necessary to be able to continuously identify and analyze different components, systems and communication protocols. In this work, we conducted a threat analysis for the smart home ecosystem, utilizing the smart home reference architecture of [PERSON] et al. [31], the STRIDE threat analysis method and Microsoft's threat modeling tool, with an eye towards identifying and analyzing potential threats which target both physical components of a smart home environment and data flows among them. The analysis considered six smart home instances of varying complexity. We later developed a smart home network topology generator and a malware propagation graph-based model to effectively evaluate the effects of the dynamic properties of the environment in specific security threats. Figure 11: Simulation network architecture. Even though the proposed approach has been demonstrated to allow the capture of dynamic changes of devices and/or back-end services in a smart home environment, it does not come without limitations. These are mostly related to the threat analysis tool, which has not managed to identify denial of service threats and cannot handle physical threats that might affect the physical infrastructure. Furthermore, the existing DFD template does not support all the communication protocols that may be used in a smart home; hence, our analysis was limited only to the ZigBee, HTTPS and 4G protocols. Moreover, the analysis of existing communication protocols considered only spoofing, tampering and elevation of privileges threats, as repudiation and information disclosure threats are not fully supported. What is more, the currently available template does not allow the examination of transitive attacks over the network. Specifically, the specific template analyzes each component of the DFD separately and cannot consider malicious actions which, for example, could occur by a stealthy malware. Despite these limitations, the approach can be used to provide input to one of the dynamic risk assessment methods to identify potential risks to the physical and communication viewpoint. Results from the attack model algorithm show that the presence of dynamic nodes can change the propagation pattern and rate of a malware, with effects directly proportional to the initial and final neighborhood size of the mobile nodes. The presence of intermittently available nodes also has an impact on the network. With a higher number of intermittent nodes in a network, the final rate of propagation of a malware in otherwise identical conditions is reported to be slower and with a smaller expansion. A network simulation, to showcase real-time malware propagation in a smart home network, was conducted to validate the outputs of the models developed. The results of the simulation confirm that intermittent availability of nodes impact malware propagation. The simulation also highlights that mobility patterns of dynamic nodes should be taken into consideration when developing network topology generators and attack models, as they would allow for more realistic prediction of the neighborhood size and topological position of infected nodes. As future work, we intend to develop a more flexible template to reflect the smart home ecosystem in higher fidelity, and use this to carry out a refined threat analysis, to be used as input to the dynamic risk assessment approach taken in the GHOST project [16]. To this end, we will analyze the security of the communication protocols and data packets in more detail in order to contribute to the most crucial part of the Risk Engine, namely risk analysis. The more rigorous threat analysis results will be used for Contextual Profiling (CP) as described in the GHOST architecture. In particular, the development of the classification templates and profiles of the typical Smart Home devices will be enhanced, by leveraging the results herein in the training process.