Biblio

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.

Threat modeling is one of the best practices to secure software development. A primary challenge for using this practice is how to extract threats. Existing threat extraction methods to this purpose are mainly based on penetration tests or vulnerability databases. This imposes a non-automated timeconsuming process, which fully relies on the human knowledge and expertise. In this paper, a method is presented, which can extract the threats to a software system based on the existing description of the software behavior. We elaborately describe software behavior with sequence diagrams enriched by security relevant attributes. To enrich a sequence diagram, some attributes and their associated values are added to the diagram elements and the communication between them. We have also developed a threat knowledge base from reliable sources such as CWE and CAPEC lists. Every threat in the knowledge base is described according to its occurrence conditions in the software. To extract threats of a software system, the enriched sequence diagrams describing the software behavior are matched with the threat rules in our knowledge base using a simple inference process. Results in a set of potential threats for the software system. The proposed method is applied on a software application to extract its threats. Our case study indicates the effectiveness of the proposed method compared to other existing methods.