Biblio

In recent years, several trust management frame-works and models have been proposed for the Internet of Things (IoT). Focusing primarily on distributed trust management schemes; testing and validation of these models is still a challenging task. It requires the implementation of the proposed trust model for verification and validation of expected outcomes. Nevertheless, a stand-alone and standard IoT network simulator for testing of distributed trust management scheme is not yet available. In this paper, a .NET-based Distributed Trust Management Simulator for IoT Networks (DTMSim-IoT) is presented which enables the researcher to implement any static/dynamic trust management model to compute the trust value of a node. The trust computation will be calculated based on the direct-observation and trust value is updated after every transaction. Transaction history and logs of each event are maintained which can be viewed and exported as .csv file for future use. In addition to that, the simulator can also draw a graph based on the .csv file. Moreover, the simulator also offers to incorporate the feature of identification and mitigation of the On-Off Attack (OOA) in the IoT domain. Furthermore, after identifying any malicious activity by any node in the networks, the malevolent node is added to the malicious list and disseminated in the network to prevent potential On-Off attacks.

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. An effective model for indirect trust computation is proposed which is capable of identifying dishonest recommendations. Dishonest recommendations are identified by using deviation based detecting technique. The concept of measuring the credibility of recommendation (rather than credibility of recommender) using fuzzy inference engine is also proposed to determine the influence of each honest recommendation. The proposed model has been compared with other existing evolutionary recommendation models in this field, and it is shown that the model is more accurate in measuring the trustworthiness of unknown entity.