A lightweight trust management algorithm based on subjective logic for interconnected cloud computing environments

Kurdi, Heba; Alfaries, Auhood; Al-Anazi, Abeer; Alkharji, Sarah; Addegaither, Maimona; Altoaimy, Lina; Ahmed, Syed Hassan

doi:10.1007/s11227-018-2669-y

Cited by 38 publications

(27 citation statements)

References 39 publications

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“…Two state-of-the-art algorithms, namely, EigenTrust [ 24 ] and InterTrust [ 25 ], and a reference case with no trust algorithm (none) were employed to benchmark the proposed algorithm performance.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…In identifying malicious peers, trust and reputation management systems follow different approaches [ 25 ], such as trust vectors [ 24 ], subjective logic [ 25 ] and machine learning [ 8 ]. EigenTrust [ 24 ] is one of the most commonly used trust management algorithms based on a normalized trust vector.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Opinions provide accurate trust information about peers; however, the main drawback of TNA-SL is its exponential running time complexity due to the lengthy matrix chain multiplication process for transitive trust calculations. Thus, InterTrust [ 25 ] was proposed to overcome this issue. InterTrust maintains the advantages of the original TNA-SL while being scalable and lightweight with low computational overhead by using more scalable data structures and reducing the need for matrix chain multiplications.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To bridge this gap, this paper proposes Trutect, which is a trust management system that uses the power of neural networks to detect malicious peers and identify their specific model and other group members, if any. The proposed system is thoroughly evaluated and benchmarked based on rival trust management systems, including EigenTrust [ 24 ] and InterTrust [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

Alhussain

Kurdi

Altoaimy

2021

Sensors

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Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model.

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Section: Evaluation Methodologymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

Alhussain

Kurdi

Altoaimy

2021

Sensors

Self Cite

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“…Aref et al 17 proposed a hybrid trust model based on reinforcement learning and fuzzy logic, which uses fuzzy logic to summarize trust factors and uses reinforcement learning to evaluate trust. Kurdi et al 18 proposed a lightweight trust management algorithm based on subjective logic, which improves the TNA‐SL algorithm to make it more suitable for overlaying trust relationships in a cloud environment. Li et al 19 proposed an evaluation model of service trust based on clustering fuzzy reasoning, which establishes a cluster evaluation mechanism based on a fuzzy membership function by determining four cluster evaluation indices.…”

Section: Related Workmentioning

confidence: 99%

A trust management model based on mutual trust and a reward‐with‐punishment mechanism for cloud environments

Guo

Yang

Luan

et al. 2021

Concurrency and Computation

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Aiming at overcoming problems such as malicious entities and trust crises in cloud environments, a trust management model based on mutual trust and a rewardwith-punishment mechanism is proposed in this paper. First, according to reputation values of entities and trust relationships among entities, we calculate the comprehensive trust values of a service request user to several candidate service providers and probe these providers with high comprehensive trust values. Second, we select a trade provider according to mutual trust. Finally, after the trade, we update the reputation values and other data of the request user, trade provider, and recommenders based on the final trade result to reward or punish them to various degrees, thus ultimately reducing malicious or dishonest behavior. The experimental results show that our model can effectively identify malicious entities to increase the trade success rate. K E Y W O R D Scloud environment, mutual trust, reputation, reward-with-punishment INTRODUCTIONCloud computing is a new network application model that was proposed by Google in 2006. Cloud computing was developed based on a series of traditional technologies, such as grid computing, distributed systems, peer to peer (P2P), and virtualization. It is a new information technology service resource. It distributes computing tasks over a resource pool that is composed of many computers, thereby enabling various application systems to obtain computing power, storage space, and information services as needed and providing dynamically scalable and cheap computing services on demand through the network. 1,2 Cloud computing can be divided into the following three categories according to the service model: IaaS (infrastructure as a service), PaaS (platform as a service), and SaaS (software as a service). 3 However, the widespread application of cloud computing technology also faces many security threats, such as privacy theft, resource misappropriation, virus attacks, and user malicious behavior attacks. The cloud environment is open, and users can add, delete, modify, and check data through the network. If users who passed the identity authentication behave maliciously, a major challenge will be posed to cloud computing security. Therefore, each user's behavior must be evaluated to identify the malicious users.In addition, there is a crisis of trust between cloud service providers and users: Users have doubts regarding whether cloud service providers can provide qualified cloud services in trade (in this paper, when a user buys and uses a provider's service, we say there is a εtradeε between the user and the provider, and the provider is called the trade provider.), while providers also have doubts about users. Therefore, to ensure the smooth operation of trades, the trust relationship between users and providers must be evaluated. Although researchers have proposed various trust models, most models consider only the attitude of user in the trade and ignore that of provider, or do not consider the impact of trade prices ...

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Trust Management Methodology and Agents Simulations Framework for Conflict Research

Trček

2023

Advcd Theory and Sims

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Trust plays a pivotal role in numerous humans’ interactions, and it affects relationships ranging from individuals to interstate levels. Any conflict and its resolution can therefore benefit from insights into trust dynamics. The deployment of computational trust management methods and agent technologies provide promising options. They have already been successfully applied in many domains, while in conflict research (and possible resolution policies) they are rather unexplored. This research paper therefore presents an approach that is based on a formalized, anthropocentric trust management method. It deploys it accordingly in agents’ simulations to obtain new insights into the dynamics of trust in cooperation–conflict interplay, and to enable derivation of strategies for managing societies to drive them toward desired states.

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A lightweight trust management algorithm based on subjective logic for interconnected cloud computing environments

Cited by 38 publications

References 39 publications

Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

A trust management model based on mutual trust and a reward‐with‐punishment mechanism for cloud environments

Trust Management Methodology and Agents Simulations Framework for Conflict Research

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