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Comparison IoT architecture with optimized configuration by using MQTT protocol B. Secured Transaction Framework Slightly, there is no difference between the proposed topol-ogy and the conventional IoT topology. As shown in Fig-ure. 2b, this research recommends lightweight cryptography algorithm for IoT devices to camouflage the messages from sensor node to web platform or to prevent the possibility of the publisher's data stealing (unregistered Raspberry Pi device). The implementation of costumed Fernet library at the publisher's, broker's, subscriber's, and
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Internet of Things (IoT) has been augmenting the emerging technologies and certainly been varying our daily life. The adoption of this technology is strengthened by the growth of connecting devices as shown in recent literature. However, responsibility related to secure communication also needs to increase as the number of connections grows. For in...
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Internet of Things (IoT) systems have gained huge popularity in the past decade. This technology is developing as a back boon from the day-to-day utility in smart homes to intelligent power grids. It has become ubiquitous in the past decade while gaining popularity in academia and industry. As the devices used are usually sensors without a well-dev...
Citations
... For this stage, we developed a qualitative analysis of the 55 documents from the eligibility analysis phase using ATLAS TI version 9. During the qualitative analysis, 11 codes were defined in the Atlas TI associated with factor: application domain, related to the verticals in which IoT systems have been implemented [22][23][24]; attack surface, related to the entry and exit points via which attacks can be performed [25]; interdependency, related to the relationship of the IoT system with other IT/OT/IoT systems that could increase the severity of the attack [26]; scalability, related to the coverage area that can be affected by the propagation of the attack [27]; severity, related to the value of the damage that can be caused by the attack [28]; susceptibility, related to the predisposition to pick up the effects of an attack [29]; type of attack, related to the attack vector, technique or methodology [30,31]; device type, related to the type of IoT device [32,33]; type of information, related to the type of information processed, stored, or transmitted by the device [34]; uncertainty, related to the unknown factors that could affect the security of IoT systems [35]; vulnerabilities, related to the weak points that IoT systems may have and that may increase the possibility of being affected by an attack [36][37][38][39][40][41]. The density values of the codes (factors) are shown in Figure 4. ...
... For this stage, we developed a qualitative analysis of the 55 documents from the eligibility analysis phase using ATLAS TI version 9. During the qualitative analysis, 11 codes were defined in the Atlas TI associated with factor: application domain, related to the verticals in which IoT systems have been implemented [22][23][24]; attack surface, related to the entry and exit points via which attacks can be performed [25]; interdependency, related to the relationship of the IoT system with other IT/OT/IoT systems that could increase the severity of the attack [26]; scalability, related to the coverage area that can be affected by the propagation of the attack [27]; severity, related to the value of the damage that can be caused by the attack [28]; susceptibility, related to the predisposition to pick up the effects of an attack [29]; type of attack, related to the attack vector, technique or methodology [30,31]; device type, related to the type of IoT device [32,33]; type of information, related to the type of information processed, stored, or transmitted by the device [34]; uncertainty, related to the unknown factors that could affect the security of IoT systems [35]; vulnerabilities, related to the weak points that IoT systems may have and that may increase the possibility of being affected by an attack [36][37][38][39][40][41]. The density values of the codes (factors) are shown in Figure 4. From the quality analysis, we identified 11 factors of IoT devices that could affect that security risk value. ...
IoT systems contribute to digital transformation through the development of smart concepts. However, the IoT has also generated new security challenges that require security tools to be adapted, such as risk analysis methodologies. With this in mind, the purpose of our study is based on the following question: Which factors of IoT devices should be considered within risk assessment methodologies? We have addressed our study with a 4-phase design-research methodology (DRM) that allows us, based on systematic literature review, to experiment and draw upon expert judgment; as a final product, we obtain a risk assessment methodology based on the characteristics of IoT devices. At the end of this study, we establish seven main constructs—Organization, Risk Behaviors, Dependency, Attack Surface, Susceptibility, Severity and Uncertainty—over which security risk in IoT systems can be evaluated.
