Distributed Trust Management Model Research Articles

T-FedHA: A Trusted Hierarchical Asynchronous Federated Learning Framework for Internet of Things

Federated Learning (FL) is a distributed machine learning system designed to effectively address potential data privacy concerns, making it particularly promising for the Internet of Things (IoT). However, challenges arise due to the trust and heterogeneity of IoT devices. FL often assumes devices are trustworthy and curious, which is impractical in the realistic IoT environment where the trustworthiness of participating devices becomes an intractable problem. Additionally, the diverse computational power and communication efficiency across IoT devices impact the efficiency and performance of FL. To address these challenges and enable the effective implementation of FL in the IoT environment, this article proposes a Trusted Hierarchical Asynchronous Federated Learning Framework (T-FedHA). The framework introduces a “Device-Edge-Central” hierarchy and employs a distributed trust management model. This model manages the trust of IoT devices, selecting devices that meet trust requirements for FL training. This enhances the security and trustworthiness of the overall FL system. Furthermore, T-FedHA integrates standard FL methods with a hierarchical asynchronous strategy that includes edge servers. This approach improves model aggregation and update processes, employing an asynchronous technique that considers the staleness of model updates to enhance overall performance and effectiveness. Extensive experiments confirm the efficacy of T-FedHA. The distributed trust management model efficiently calculates and manages IoT device trust, allowing only trusted devices to participate in FL training. The hierarchical asynchronous approach proves more feasible and efficient, as well as robust and flexible, contributing to the reliability of the proposed framework.

Construction of Credit based Distributed Trust Management Model for P2P Network

Construction of Credit based Distributed Trust Management Model for P2P Network

A Distributed Trust Management Mechanism for the Internet of Things Using a Multi-Service Approach

In the Internet of Things (IoT) heterogeneous devices can cooperate and communicate to provide or require determined services. A multi-service IoT is vulnerable to many types of malicious attacks. A trust management scheme is a strategy to establish trust between devices. In this work we propose a distributed trust management model for IoT using direct and indirect observations. The trust of a node is computed by the service quality and recommendations from neighbors. The nodes locally compute the trust of their neighbors, without the need of a central entity. We implemented the proposed strategy in the Cooja simulator of the Contiki operating system to analyze the performance of the trust model. We performed simulations using different number of malicious nodes performing the bad mouthing attack. Simulation results show the trust management scheme is able to detect malicious nodes in the network.

Effective distributed trust management model for Internet of Things

The IoT (Internet of Things) is defined as a global infrastructure for the information society, which provides advanced services by interconnecting physical or virtual objects through existing interoperable information and communication technologies in evolution. To note, the explosion of the number of smartphones and connections has created a new market with almost infinite opportunities. In 2016, 5.5 million objects are connected every day in the world. A number that could quickly reach billions, by 2020 [1]. Gartner predicts that 26 billion objects will be installed in 2020. Other evaluations consider that a human being would interact with 1,000 to 5,000 objects during a normal day. The market for connected objects could range from a few tens of billions to up to several thousand billion units. Among the vital components of IoT, we find wireless sensor networks. WSNs allow the representation of dynamic characteristics of the real world in the virtual world of the Internet. Nevertheless, the opening of these types of network to the Internet presents a serious problem stand point security. The introduction of intrusion detection mechanisms is essential to limit the various attacks that threaten the proper functioning of the networks.In this work, we propose a new intrusion detection model for Internet of Things, specifically for WSNs. This model relies on a geographic location check of the nodes to make sure that we communicate with the right node for each transaction. Subsequently, we proposed rules for detecting attacks. A mathematical model for trust calculating was proposed to update trust nodes values and eliminate malicious nodes. The simulation results were able to show the effectiveness of our model.

A distributed trust management model for mobile P2P networks

In this paper we propose a distributed Trust Management Model (DTMM) for constructing much shorter and more robust trust chains in mobile p2p networks. With DTMM, each moving object within the same group tends to have a high probability of keeping stable distances from each other. The main contribution of this model is to predict the future availability of wireless links and lead to fast generating valid trust evidences. A major difference between DTMM and many existing models in trust literature is that DTMM does not rely on any fixed networking infrastructure, and therefore is able to adapt to both high mobility environments. Our work appears to be the first attempt to research the relationship between mobile patterns and trust management of peers in mobile P2P network. The simulation results show that our model is highly robust and scalable in the dynamic environment of mobile networks.