A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their applications.Therefore,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN applications.This paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering packets.The proposed version employs a network-based reputation system to select the best and most secure path to a destination.To achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation system.To minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation tables.Moreover,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast approaches.The proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other protocols.Using the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing path.The paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different zones.Furthermore,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network(MANET).A MANET’s nodes could engage actively and dynamically with one another.However,MAN-ETs,from the other side,are exposed to severe potential threats that are difficult to counter with present security methods.As a result,several safe communication protocols designed to enhance the secure interaction among MANET nodes.In this research,we offer a reputed optimal routing value among network nodes,secure computations,and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism(HTRM).In addition,the study designs a robust Public Key Infrastructure(PKI)system using the suggested trust evaluation method in terms of“key”generation,which is a crucial component of a PKI cryptosystem.We also concentrate on the solid node authenticating process that relies on pre-authentication.To ensure edge-to-edge security,we assess safe,trustworthy routes to secure computations and authenticate mobile nodes,incorporating uncertainty into the trust management solution.When compared to other protocols,our recommended approach performs better.Finally,we use simulations data and performance evaluation metrics to verify our suggested approach’s validity Our approach outperformed the competing systems in terms of overall end-to-end delay,packet delivery ratio,performance,power consumption,and key-computing time by 3.47%,3.152%,2.169%,and 3.527%,3.762%,significantly.

SLCRM： Subjective Logic-Based Cross-Layer Reputation Mechanism for Wireless Mesh Networks

Wireless Mesh Networks （WMNs） are vulnerable to various security threats because of their special infrastructure and communication mode, wherein insider attacks are the most challenging issue. To address this problem and protect innocent users from malicious attacks, it is important to encourage cooperation and deter malicious behaviors. Reputation systems constitute a major category of techniques used for managing trust in distributed networks, and they are effective in characterizing and quantifying a node＇s behavior for WMNs. However, conventional layered reputation mechanisms ignore several key factors of reputation in other layers; therefore, they cannot provide optimal performance and accurate malicious node identification and isolation for WMNs. In this paper, we propose a novel dynamic reputation mechanism, SLCRM, which couples reputation systems with a cross-layer design and node-security-rating classification techniques to dynamically detect and restrict insider attacks. Simulation results show that in terms of network throughput, packet delivery ratio, malicious nodes＇ identification, and success rates, SI_CRM imple- ments security protection against insider attacks in a more dynamic, effective, and efficient manner than the subjective logic and uncertainty-based reputation model and the familiarity-based reputation model.

Reputation-Based Hierarchically Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.

A Formal Reputation System for Trusting Wireless Sensor Network

In this paper, a formal system is proposed based on beta reputation for the development of trustworthy wireless sensor networks （FRS-TWSN）. Following this approach, key concepts related to reputation are formal described step by step for wireless sensor networks where sensor nodes maintain reputation for other sensors and use it to evaluate their trustworthiness. By proving some properties of beta reputation system, the beta distribution is founded to fit well to describe reputation system. Also, a case system is developed within this framework for reputation representation, updates and integration. Simulation results show this scheme not only can keep stable reputation but also can prevent the system from some attacks as bad mouthing and reputation cheating.

A Novel Negative Multinomial Distribution Based Energy-Efficient Reputation Modeling for Wireless Sensor Networks(WSNs)

In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.

Negative Binomial Distribution Based Reputation Direct Update for Wireless Sensor Networks

In the reputation modeling of wireless sensor networks（WSNs） many literatures have proposed creative reputation indirect update methods,such as reputation integration,discounting,aging to eliminate,and filtering malicious reputation information. However,few have discussed the reputation direct update. In this paper,based on sound statistical theories,a negative binominal distribution method in the reputation direct update for WSNs is proposed. Results show that the proposed method is more suitable and time-saving for the reputation update of the resource constraint WSNs and can improve the computation power efficiency as well.

A Load Service System with Reputation Proof in P2P Networks

In this paper, a new system design for load services in computer networks with a new reputation system is constructed. The use of the reputation system is to address the free-rider problem. Database systems are used by directory agents to save information provided by load-server agents. Protocols are built for how a host finds available servers for load service or load transfer, especially when a host moves to a new region. Detailed procedures include how a directory agent builds its database, how a load-server agent provides services, and how a load-client agent receives its desired services. The system uses the fuzzy logic control method to transfer loads for load balancing, instead of the method of a fixed threshold level. For an ad-hoc (wireless) computer network framework, this new system structure is aimed to provide efficient ways for hosts to communicate with one another and to access resources in the system. This will also help the users of networks locate resources in a most effective and secure manner.

Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network

Under the development of computing and network convergence,considering the computing and network resources of multiple providers as a whole in a computing force network(CFN)has gradually become a new trend.However,since each computing and network resource provider(CNRP)considers only its own interest and competes with other CNRPs,introducing multiple CNRPs will result in a lack of trust and difficulty in unified scheduling.In addition,concurrent users have different requirements,so there is an urgent need to study how to optimally match users and CNRPs on a many-to-many basis,to improve user satisfaction and ensure the utilization of limited resources.In this paper,we adopt a reputation model based on the beta distribution function to measure the credibility of CNRPs and propose a performance-based reputation update model.Then,we formalize the problem into a constrained multi-objective optimization problem and find feasible solutions using a modified fast and elitist non-dominated sorting genetic algorithm(NSGA-II).We conduct extensive simulations to evaluate the proposed algorithm.Simulation results demonstrate that the proposed model and the problem formulation are valid,and the NSGA-II is effective and can find the Pareto set of CFN,which increases user satisfaction and resource utilization.Moreover,a set of solutions provided by the Pareto set give us more choices of the many-to-many matching of users and CNRPs according to the actual situation.

Blockchain-Based Decentralized Reputation Management System for Internet of Everything in 6G-Enabled Cybertwin Architecture

Internet of Everything(IoE)has emerged as a promising paradigm for the purpose of connecting and exchanging data among physical objects and humans over the Internet,and it can be widely applied in the fields of industry,transportation,commerce,and education.Recently,the emergence of 6G-enabled cybertwin network architecture provides the technical and theoretical foundation for the realization of IoE paradigm.However,the IoE has three open issues in the 6G-enabled cybertwin architecture,i.e.,data authenticity,data storage and node reliability.To address these issues,we propose a blockchain-based decentralized reputation management system(BC-DRMS)for IoE in 6G-enabled Cybertwin architecture.In the proposed BC-DRMS,the traffic data collected from end nodes is stored on the blockchain and the decentralized file system,i.e.,InterPlanetary File System(IPFS),to resist data tampering,and then the data is further processed by the edge clouds and core clouds to provide services to users.Also,a multi-level reputation evaluation scheme is designed to compute the reputation scores of IoE nodes to prevent malicious node attacks.The experiment results and analysis demonstrate that,compared to the traditional centralized reputation management systems(CRMS),the proposed BC-DRMS cannot only address the issues of data authenticity and storage,but also provides high reliability for IoE in 6G-enabled cybertwin architecture.

A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting characteristics.Nevertheless,existing NDN faces three significant challenges,including security,privacy,and routing.In particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious content.For instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road accidents.In such a situation,trust in the content-providing vehicles brings a new challenge.On the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another challenge.Moreover,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in VNDN.In this connection,our contribution is threefold.Unlike existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate nodes.Based on ML evaluation results,vehicles accept or discard served content.Secondly,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ledger.Finally,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)approach.We implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive Bayes.The qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

基于跨链技术的食品供需网信息共享激励模式构建

随着食品种类多元化和消费者需求的提升,食品生产流程更加复杂,供应链之间交流越来越多,相关企业、食品供应链和行业机构之间的交互形成了复杂的食品供需网结构。食品供需网成员之间信息共享有利于企业之间充分合作实现长期发展,但仍有许多企业共享信息意识较弱,无法进行有效信息沟通。区块链跨链技术的应用和发展,为食品供需网信息共享提供了新思路。根据食品供需网复杂性,引入区块链技术和声誉激励机制,将声誉进行量化管理,结合区块链智能合约技术实现声誉激励机制自动化。进一步引入区块链跨链技术,打破不同行业、地区区块链之间的交易壁垒,构建基于区块链跨链技术的食品供需网信息共享声誉激励模式,以猪肉供需网为例阐述跨链信息共享声誉激励流程。该激励模式不仅保障共享信息安全性与准确性,同时保护企业隐私,最大程度保护交易方自身利益,破解信息无法共享的障碍,激发食品供需网企业参与信息共享积极性。不仅实现食品供需网内部异构区块链之间的信息交流和价值互通,还为食品供需网价值信息交流互通的发展提供新思路。

商会关系、声誉机制与代理成本

以中国证券市场2004—2021年A股上市公司为研究对象,研究企业家(董事长或总经理)商会关系资本与企业代理成本之间的关系,并进一步考察企业家个人声誉机制对上述关系的潜在影响。研究结果表明:企业家商会关系能够降低企业代理成本,且企业家商会任职层级越高、任职种类越多,抑制作用越显著;当企业家拥有人大代表或政协委员的政治身份以及兼任其他上市公司董事的社会身份等个人声誉时,商会关系对企业代理成本的抑制作用较弱;渠道检验发现,激励机制和监督机制是企业家商会关系作用于企业代理成本的重要渠道;进一步分析发现,企业家行业协会关系、国家及省级商会关系对代理成本的抑制作用更加明显,在非国有企业中企业家商会关系对代理成本的抑制作用更加明显。研究结论丰富和拓展了关系社会资本的微观经济后果及作用机制,为深入理解商会关系文化对微观企业决策行为的作用机制提供了初步的理论分析框架和经验证据。

声誉损失、违约罚金与制造企业间低碳创新网络治理:基于演化博弈模型

针对当前我国企业间“低碳创新,协同发展”所面临的激励低效问题,基于声誉损失与违约罚金视角,构建制造企业间的低碳创新合作演化博弈模型,仿真分析在声誉损失机制和违约罚金机制作用下合作主体行为策略的演变规律。仿真结果表明:(1)当未设置违约罚金或声誉损失较小时将不利于低碳创新网络的长久发展,创新主体双方均会出现机会主义从而选择终止合作关系;(2)“强声誉机制”可以使低碳创新网络更稳定,声誉损失治理机制对制造企业的策略选择起到正向影响作用,制造企业的声誉损失越大,则其背叛成本也就越高;(3)适度的违约罚金机制能够加强创新主体间的协同绩效,而过高或过低的违约罚金均不利于低碳创新网络的长久发展。因此,该研究可以为制造企业间实现低碳创新合作建立声誉激励与违约罚金机制提供一定程度的理论依据。

MC-RHotStuff:面向多链基于信誉的HotStuff共识机制

现有区块链呈现出多链趋势,传统共识算法不具备面向多链的动态扩展性,难以应对大规模联盟链开放使用与封闭运维的矛盾.对此,提出了一种新颖的多链共识方法MC-RHotStuff:节点划分不同的角色,分为待准入节点、备选节点、共识节点,每条工作链都拥有共识节点和备选节点,待准入节点完成准入验证后将成为备选节点;共识节点拥有其他节点不具备的信誉值,做出正确行为的共识节点将提高信誉值,做出错误行为的共识节点将扣除信誉值,通过节点信誉计算及筛选算法MC-Scan来寻找信誉值异常的节点,并从备选节点中选择新的共识节点与异常节点交换.此外,还提出了节点动态调整算法MCSchedule,通过检测每条区块链的交易量从而动态地调整共识节点的数量来达到最优化,既保证区块链系统的高效执行,又提高了节点筛选的速度.为保证当节点数量变化或共识群组成员发生变化时,共识机制能够正常运转,提出了节点状态同步机制MC-Syn.对此进行了大量的实验来验证MC-RHotStuff性能,与现有系统相比,其交易吞吐量和延迟综合提升约15%.

基于改进Raft共识算法和PBFT共识算法的双层共识算法

针对目前应用于联盟链中的实用拜占庭(PBFT)共识算法可扩展性不足、通信开销增长过大、难以适用于大规模网络节点环境等问题,提出了一种基于改进Raft共识算法和PBFT共识算法的双层共识算法(DL_RBFT)。首先将区块链中的节点分成若干小组,组成下层共识网络,然后小组的组长再构成上层共识网络,形成一个双层共识网络结构;在下层共识网络的小组内部引入监督机制和声誉机制来改进Raft共识算法,在初始组长的选举流程引入了蚁群算法,使选举效率始终维持在较高水平;在上层共识网络中,使用PBFT共识算法进行共识。改进后的Raft共识算法具备了抗拜占庭节点攻击的能力,提升了算法的安全性。实验结果分析表明,相较于传统的PBFT共识算法,在100个节点的情况下,DL_RBFT将共识时延降低了两个数量级,吞吐量也提升了一个数量级,与其余改进算法相比也有着明显优势。因此DL_RBFT共识算法拥有良好的可扩展性,可以广泛应用于联盟链的各种场景中。

漏洞预测下级联网络安全态势感知研究

级联网络的节点信誉度呈现较大差别,会出现较多随机漏洞,导致安全态势评估精度较低。为了提升级联网络安全态势感知精度,提出漏洞预测下级联网络安全态势感知算法。通过布隆过滤器过滤冗余安全态势要素,基于概率统计理论计算和更新节点信誉度,完成数据融合处理。创新性地通过漏洞预测算法实时预测未来漏洞数量,配合预定义的安全策略实施全局融合处理。基于贝叶斯攻击图集合,预测攻击者后续攻击行为。融合安全态势要素和攻击行为预测结果,并以此为依据计算级联网络安全态势,实现级联网络安全态势感知。测试结果表明,所提算法可以准确检测到网络中的全部攻击,报警次数和真实报警次数基本吻合,并可以获取高精度的级联网络安全态势级联融合感知结果。该算法在提升网络安全方面具有有效性和可行性。

基于战车和夺控点分配的智能体步兵投送策略决策方法

针对陆战兵棋开局阶段智能体如何分配战车和如何选择步兵投送点位,提出基于战车和夺控点分配的步兵投送策略决策方法。使用多目标最优规划模型将战车部署问题建模为多目标分配问题,构建目标收益函数,引入改进合同网协议算法对问题进行求解;通过分析不同理性程度智能体的行为和意图,将步兵投送点位选择问题建模为多策略分配问题,给出混合投送点位选择算法。实验证明所提方法可解释性强,同时具有一定的针对性、灵活性。

METrust: A Mutual Evaluation-based Trust Model for P2P Networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer （P2P） network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers＇ evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers＇ malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers＇ own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

An Extended Hierarchical Trusted Model for Wireless Sensor Networks

Cryptography and authentication are traditional approach for providing network security. However, they are not sufficient for solving the problems which malicious nodes compromise whole wireless sensor network leading to invalid data transmission and wasting resource by using vicious behaviors. This paper puts forward an extended hierarchical trusted architecture for wireless sensor network, and establishes trusted congregations by three-tier framework. The method combines statistics, economics with encrypt mechanism for developing two trusted models which evaluate cluster head nodes and common sensor nodes respectively. The models form logical trusted-link from command node to common sensor nodes and guarantees the network can run in secure and reliable circumstance.