Threat Extraction in IoTBased Systems Focusing on Smart Cities IoTbased services are widely increasing due to their advantages such as economy, automation, and comfort. Smart cities are among major applications of IoTbased 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 IoTbased 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 thirtytwo distinct threat types. Secondly, we distinguish threat root causes by associating a threat to constituent parts of the IoTbased 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 EHealth system and identify more than 50 threat instances in each cases to show that the method can be easily generalized for other IoTbased systems whose constituent parts are known.

Towards a Threat Model for Vehicular Fog Computing Security is a huge challenge in vehicular networks due to the large size of the network, high mobility of nodes, and continuous change of network topology. These challenges are also applicable to the vehicular fog, which is a new computing paradigm in the context of vehicular networks. In vehicular fog computing, the vehicles serve as fog nodes. This is a promising model for latencysensitive and locationaware services, which also incurs some unique security and privacy issues. However, there is a lack of a systematic approach to design security solutions of the vehicular fog using a comprehensive threat model. Threat modeling is a stepbystep process to analyze, identify, and prioritize all the potential threats and vulnerabilities of a system and solve them with known security solutions. A welldesigned threat model can help to understand the security and privacy threats, vulnerabilities, requirements, and challenges along with the attacker model, the attack motives, and attacker capabilities. Threat model analysis in vehicular fog computing is critical because only brainstorming and threat models of other vehicular network paradigms will not provide a complete scenario of potential threats and vulnerabilities. In this paper, we have explored the threat model of vehicular fog computing and identified the threats and vulnerabilities using STRIDE and CIAA threat modeling processes. We posit that this initiative will help to improve the security and privacy system design of vehicular fog computing.