Exploring the trust management mechanism in self-organizing complex network based on game theory

Owing to the characteristics of underwater communication, such as limited bandwidth, low transmission speed, and long delivery delay, it is significantly challenging to address trust management in the Internet of Underwater Things (IoUT). In the process of trust calculation, trust judgments between nodes may be conflicting based on the obtained diverse trust evidences. However, in existing studies, the analysis of trust-conflict adjudication is nonexhaustive and lacking in detail. Therefore, a controversy-adjudication method is proposed in this study to handle conflict recommendations, and a novel trust management mechanism is further investigated based on the controversy-adjudication method, including three phases: 1) trust calculation; 2) trust recommendation; and 3) trust evaluation. First, trust evidences, e.g., packet delivery ratio, end-to-end packet transmission latency, and residual energy, are collected to calculate trust for trustees. In addition, for the trustor without sufficient trust evidences, recommendations are required and an incentive mechanism is proposed based on the prisoner’s dilemma to encourage neighbors to participate in trust recommendation. Finally, trust values of trustees are obtained by executing trust evaluation based on the controversy-adjudication mechanism. Simulation results demonstrate that the proposed trust management mechanism outperforms existing related works in terms of accuracy and robustness against unreliable IoUT.

Trust management is fundamental to identify malicious, selfish, and compromised nodes which have been authenticated. Many trust management mechanisms are proposed to provide effective security solution for wire and wireless networks, even some of them have become one of the most known in this field. But in fact, trust management in wireless sensor networks still remains a challenging field. Traditional trust models developed for wireless sensor networks are not well suited for sensor networks because the trust evaluation process isn’t related to certain application. In this paper, we propose a self-adaptive trust management scheme, which can adaptively adjust the trust evaluation process to cope with application requirement changes of trust management. The simulation results show that our scheme has less rate of inauthentic downloads of nodes, energy consumption and message overheads as compared to the current state-of-the-art trust management schemes, and it is more suitable for large-scale sensor networks.

The rapid development and increasing popularity of mobile devices have facilitated the development of computation‐intensive applications and services, but their constrained compute, storage and battery capabilities have also created the requirements for optimized resource provision and service orchestration. Emerging mobile edge computing (MEC) and next‐generation networks (6G) can meet these requirements of mobile devices by migrating complex computing tasks and large volumes of data to edge servers with acceptable latency, thus effectively enhancing the quality of experience (QoE) of mobile users. Intelligent Vehicular Collaboration, as an intelligent mobile application or service in intelligent connected vehicle (ICV) applications, enables precise and effective driving decisions based on a large amount of real‐time traffic information statistics and intelligent analysis of vehicle status to improve the user driving experience and the efficiency of intelligent transportation systems(ITS). However, the openness and dynamics of wireless communications and the high frequency of information interaction of MEC‐enabled intelligent vehicular collaboration services in the 6G era make them highly vulnerable to cyber attacks. How to achieve dynamically trustworthy, secure, and intelligent vehicular collaboration services in the presence of malicious node access and cyber attacks in the 6G MEC‐enabled vehicular network is a significant challenge. In this paper, we propose a novel blockchain‐enabled trust management mechanism to address this challenge. First, we design a spatiotemporal‐correlated message credibility assessment method and a dynamic mutual trust evaluation method to obtain accurate ratings for messages and mobile nodes respectively. Based on the above methods, we propose an improved blockchain‐based intelligent trust management mechanism that enables transparent, tamper‐evident, and trustworthy secure intelligent vehicular collaboration services while maintaining stable and efficient system performance. Simulation results show that our suggested solutions can efficiently evaluate the trust level of ICVs and achieve efficient and secure intelligent vehicular collaboration with acceptable communication and computation overheads.

Ranking systems such as those in product review sites and recommender systems usually use ratings to rank favorite items based on both their quality and popularity. Since higher ranked items are more likely selected and yield more revenues for their owners, providers of unpopular and low quality items have strong incentives to strategically manipulate their ranking. This paper analyzes the adversary cost for manipulating these rankings in a variety of scenarios. Particularly, we analyze and compare the adversarial cost to attack ranking systems that use various trust measures to detect and eliminate malicious ratings to systems that use no such a trust management mechanism. We provide theoretical results showing the relation between the capability of the trust mechanism in detecting malicious ratings and the minimal adversarial cost for successfully changing the ranking. Furthermore, we study the impact of sharing trust information between ranking systems to the adversarial cost. It is proved that sharing information between two ranking systems on common user identities and malicious behaviors detected can increase considerably the minimal adversarial cost to successfully attack the two systems under certain assumptions. The numerical evaluation of our results shows that the estimated adversary cost for manipulating the item ranking can be made significant when proper trust mechanisms are employed or combined.

As a new networking paradigm, opportunistic networking communications have great vision in animal migration tracking, mobile social networking, network communications in remote areas and intelligent transportation, and so on. Opportunistic networks are one of the evolutionary mobile ad hoc networks, whose communication links often suffer from frequent disruption and long communication delays. Therefore, many opportunistic forwarding protocols present major security issues, and the design of opportunistic networks faces serious challenges such as how to effectively protect data confidentiality and integrity and how to ensure routing security, privacy, cooperation, and trust management. In this paper, we first systematically describe the security threats and requirements in opportunistic networks; then propose a general security architecture of opportunistic networks; and then make an in‐depth analysis on authentication and access control, secure routing, privacy protection, trust management, and incentive cooperation mechanisms; and at the same time, we present a comparison of various security and trust solutions for opportunistic networks. Finally, we conclude and give future research directions. Copyright © 2014 John Wiley & Sons, Ltd.

This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework aimed at bolstering the security of Internet of Things (IoT) environments by synergistically integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The proposed hierarchical architecture spans edge, fog, and cloud layers, ensuring efficient and scalable collaborative intrusion detection. Trustworthiness is established through the incorporation of distributed ledger technology (DLT), leveraging blockchain frameworks to enhance the reliability and transparency of communication among IoT devices. Furthermore, the research adopts federated learning (FL) techniques to address privacy concerns, allowing devices to collaboratively learn from decentralized data sources while preserving individual data privacy. Validation of the proposed approach is conducted using the CICIoT2023 dataset, demonstrating its effectiveness in enhancing the security posture of IoT ecosystems. This research contributes to the advancement of secure and resilient IoT infrastructures, addressing the imperative need for lightweight, trust-managing, and privacy-preserving solutions in the face of evolving cybersecurity challenges. According to our experiments, the proposed model achieved an average accuracy of 97.65%, precision of 97.65%, recall of 100%, and F1-score of 98.81% when detecting various attacks on IoT systems with heterogeneous devices and networks. The system is a lightweight system when compared with traditional intrusion detection that uses centralized learning in terms of network latency and memory consumption. The proposed system shows trust and can keep private data in an IoT environment.

In participatory sensing, the task center is not always trustworthy. It may try to profile the participants by data mining, which is a great threat to the privacy of participants. The data exchange strategy can mix data and preserve privacy of participants well. But this requires cooperation among participants. To solve this problem, a trust management mechanism is presented to encourage neighboring participants to be collaborative. And the tit for tat strategy is employed to punish malicious neighboring participants. The analysis based on game theory shows that the reasonable neighboring participants will choose the cooperative strategy in data exchange with well-designed parameter settings.

Peer-to-Peer networks are very popular these days due to their use in data and resource sharing. Trust and reputation of a peer not only make it easy to take a decision to ask for resource from the trustworthy node but also provide a way to punish evil peers for malicious act. Incentive mechanism provokes a peer to share resource and discourage the well known free riding. In this paper we studied proposed schemes and discuss some open issues to cope as the future research work as well as we have provided solution of some problems such as to detect malicious peers, false rating problem and sudden change in the behavior of peers that can be helpful in designing a new novel and robust frame work for trust and reputation based incentive mechanism.

The use of intelligent agents to improve business to business (B2B) communications is starting to be adopted by more and more companies. The main reason is that intelligent agents are able to multiply the number of agreements between suppliers and customers. However, due to the type of information managed by agents those negotiations must be protected in order to avoid fraud and misrepresentation. Communications between agents must be authenticated and, once an agreement is signed, none of the parties should be able to repudiate its signature, and hence their agreement. Furthermore, some confidentiality services must be supplied, allowing to hide some message contents or identities. Security has important implications to the participants of the market, because they will trust the system according to the level of security supplied. This paper shows how RETSINA agents may obtain those security services using trust management and security mechanisms. Our approach uses the trust infrastructure defined by [3] for RETSINA and the security services supplied by the new IPSEC protocols [1].

The existing peer-to-peer networks have several problems such as fake content distribution, free riding, white-washing and poor search scalability, lack of a robust trust model and absence of user privacy protection mechanism. Although, several trust management and semantic community-based mechanisms for combating free riding and distribution of malicious contents have been proposed by some researchers, most of these schemes lack scalability due to their high computational, communication and storage overhead. This paper presents a robust trust management scheme for P2P networks that utilizes topology adaptation by constructing an overlay of trusted peers where the neighbors are selected based on their trust ratings and content similarities. While increasing the search efficiency by intelligently exploiting the formation of semantic community structures by topology adaptation among the trustworthy peers, the scheme provides the users a very high level of privacy protection of their usage and consumption patterns of network resources. Simulation results demonstrate that the proposed scheme provides efficient searching to good peers while penalizing the malicious peers by increasing their search times as the network topology stabilizes.

A recommendation management defense mechanism based on trust model in underwater acoustic sensor networks

Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things

Many Internet of Things (IoT) networks comprise tiny devices with limited processing power and tight energy restrictions. These limitations make it difficult to use established security mechanisms protecting the devices against malicious attacks. This holds particularly for Intrusion Detection Systems (IDS) that help to detect various net-based attacks but often demand significant network and computing resources. In this article, we design and evaluate some IDS mechanisms for IoT Networks that are suited to small devices. They use a trust management mechanism that allows devices to manage reputation information about their neighbors. This mechanism makes it possible to single out maliciously behaving units in a processing and energy-friendly way. The approach is explained in the context of the healthcare domain.

The Vehicular Named Data Networking (V-NDN) improves the speed of message acquisition between vehicles and reduces network overhead by using a in-network caching mechanism. The vehicles in V-NDN have the capability of built-in caching, in other words, they can cache contents passing by and provide content services for users. However, malicious nodes in V-NDN might apply fake messages for malicious purposes, which is one of the major risks of network security. In this paper, we build a trust management mechanism based on blockchain to solve the above problems. In the proposed mechanism, vehicles first judge the credibility of the received message based on the vehicle reputation value and the feature of the message itself. Then the vehicle reputation value is updated according to the message credibility. Finally, the blockchain is used to realize the consensus of the message credibility and the vehicle reputation value. We conduct experiments on the simulation platform and simulation results show that the proposed mechanism can effectively improve the accuracy of message credibility judgment and malicious vehicles detection, thereby improving the security of the V-NDN.

Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes’ spectrum sensing results, privacy protection of sensing nodes’ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors’ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes’ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.