Context-Aware Trust and Reputation Model for Fog-Based IoT

Abstract

Trust and reputation are important terms whether the communication is Humans-to-Human (H2H), Human-Machine-Interaction (HMI) or Machine-to-Machine (M2M). As Cloud computing and the internet of things (IoT) bring new innovations, they also cause various security and privacy issues. As numerous devices are continuously integrating as a core part of IoT, it is necessarily important to consider various security issues such as the trustworthiness of a user or detection of a malicious user. Moreover, fog computing also known as edge computing is revolutionizing the Cloud-based IoT by providing the Cloud services at the edge of the network, which can provide aid in overcoming security, privacy and trust issues. In this work, we propose a context-aware trust evaluation model to evaluate the trustworthiness of a user in a Fog based IoT (FIoT). The proposed approach uses a context-aware multi-source trust and reputation based evaluation system which helps in evaluating the trustworthiness of a user effectively. Further, we use context-aware feedback and feedback crawler system which helps in making trust evaluation unbiased, effective and reliable. Furthermore, we introduce monitor mode for malicious/untrustworthy users, which helps in monitoring the behavior and trustworthiness of a user. The proposed approach uses several tunable factors, which can be tuned based on the system's requirements. The simulations and results indicate that our approach is effective and reliable to evaluate the trustworthiness of a user.

RESFIT: A Reputation and Security Monitoring Platform for IoT Applications

“…Despite these advantages, the requirement of logically linking each device in the IoT system to a central point that cannot be fully controlled by the IoT system' administrator, using cloud as the single third-layer component may not always be the best direction. To this end, a context-aware multi-source trust evaluation model interleaved with a reputation system evaluates the trustworthiness of a user in a Fog based IoT (FIoT) [21]. A monitor assistance module keeps track of untrusted users' behavior in order to identify malicious users before they start a communication.…”

Modeling of a Generic Edge Computing Application Design

“…Trust indicators may contain two notions, the subjective one (focused on the individual interests of user's interactions), and the objective one (depending on individual interaction experiences) [76]. The former is obtained from the feedback obtained out of multiple sources; whereas, the latter is done based on the quality of services, where an overall trust value determines the trustworthiness of each node, resulting in the establishment of a trusted environment [77].…”

A dynamic data encryption method based on addressing the data importance on the internet of things

“…This risk is exacerbated by the trend to separate the sensor network infrastructure and applications. Therefore, a security solution must be provided to achieve an appropriate level of security for the IoT [5]. Due to the lower cost and the time of marketing, IoT manufacturers did not give the security issue a priority to be part of their IoT devices.…”

“…Figure 1. Definition of IoT [5] Network security and data encryption are currently a very important topic in the modern communications network research areas. When we send some confidential matters from one customer to another customer that data should not be intercepted by an unauthorized person.…”