A Novel Trust Evaluation and Reputation Data Management Based Security System Model for Mobile Edge Computing Network


In recent years, Mobile Edge Computing (MEC) has arisen as a new computing platform that pushes computational power to the edge of the Internet and close to end users. Additionally, a rising number of scholars are performing various sorts of research within the framework of edge devices. The security risks of resource consumers are elevated since edge computing frequently lacks a centralized security mechanism, in contrast to cloud computing. Therefore, in this research, we focus on creating a reliable trust evaluation mechanism to overcome security concerns for enabling MEC successfully. As a result of their limited capacity for data storage and processing, these devices help pave the way for the emerging edge computing paradigm. Reputation data is processed locally on edge devices, with just the necessary data being transferred to the Cloud, which improves reliability and reduces the load on the network as a whole. There is a lack of trust amongst devices in the IoT because of the inherent security threats and assaults they face. To mitigate this threat, we offer a lightweight trust management model to oversee a device’s trustworthiness and the trustworthiness of its service levels, as well as the quality of service those levels provide (QoS). In order to determine an aggregate level of trust, the model uses QoS characteristics as weights to evaluate the trust of devices. The enhanced outcomes of QoS-parameterized trust management models suggest that they may be useful for detecting malicious edge nodes in edge computing networks, which would have practical applications in industry.

Security and Risk Analysis for Intelligent Edge Computing
Shitharth Selvarajan
Shitharth Selvarajan
Lecturer in Cyber Security

My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.