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Threat Extraction in IoT-Based Systems Focusing on Smart Cities

IoT-based services are widely increasing due to their advantages such as economy, automation, and comfort. Smart cities are among major applications of IoT-based systems. However, security and privacy threats are vital issues challenging the utilization of such services. Connectivity nature, variety of data technology, and volume of data maintained through these systems make their security analysis a difficult process. Threat modeling is one the best practices for security analysis, especially for complex systems. This paper proposes a threat extraction method for IoT-based systems. We elaborate on a smart city scenario with three services including lighting, car parking, and waste management. Investigating on these services, firstly, we identify thirty-two distinct threat types. Secondly, we distinguish threat root causes by associating a threat to constituent parts of the IoT-based system. In this way, threat instances can be extracted using the proposed derivation rules. Finally, we evaluate our method on a smart car parking scenario as well as on an E-Health system and identify more than 50 threat instances in each cases to show that the method can be easily generalized for other IoT-based systems whose constituent parts are known.