Neighbor Similarity Trust against Sybil Attack in P2P E-Commerce

Wang, Guojun; Musau, Felix; Guo, Song; Abdullahi, Muhammad Bashir

doi:10.1109/tpds.2014.2312932

Cited by 35 publications

(8 citation statements)

References 19 publications

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“…Mehta et al [22] determined that fake users are highly correlated to each other, and he proposed an algorithm based on principal component analysis. Wang et al [23] proposed an approach to eliminate maliciousness among the peers based on the neighbor similarity of peers in a group peer-to-peer (P2P) ecommerce network. These studies inspired us to propose an approach based on user similarities.…”

Section: A Robust Cf Based On Similarities For Service Recommendationmentioning

confidence: 99%

From similarity perspective: a robust collaborative filtering approach for service recommendations

Gao

Lü

Yang

et al. 2019

Front. Comput. Sci.

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Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions. We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.

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Section: A Robust Cf Based On Similarities For Service Recommendationmentioning

confidence: 99%

From similarity perspective: a robust collaborative filtering approach for service recommendations

Gao

Lü

Yang

et al. 2019

Front. Comput. Sci.

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“…By utilizing the social network structure and its dynamics, Trifunovic et al [35] proposed two supplementary methods to build social trust: dominant social trust and implicit social trust. In their article on transactions, Wang et al [36] obtained the similarity between the same group of neighbours based on the interest of one pair of partners, and they used the two points Pi and Pj to represent two neighbours and define the Jaccard similarity trust relation between two nodes through Jaccard measurement. Jin et al [37] proposed a trust model based on the evaluation of group similarity; this model can compute the global trust value with similarity between groups as the recommendation reliability, the local trust value, and global trust value can be provided by this model based on the recommended reliability, and they evaluated the model from the perspective of security.…”

Section: Similarity Trustmentioning

confidence: 99%

Consumers Team Detection Model Based on Trust for Multi-Level

Armoogum

et al. 2019

Mobile Information Systems

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Due to rapid advances in technology, social networks have become important platforms for daily communication, product marketing, and information dissemination. Targeted delivery of social network advertisement can considerably improve the efficacy of the advertisement and maximize the profits from it. In this context, managing the specific audience of a social network advertisement and achieving targeted advertisement delivery have been the ultimate goals of the social network advertising sector. Identifying user groups with similar properties is critical to increasing targeted sales. When both the scale of mobile social network and the coplexity of social network user behaviors grow, similar groups are hidden in user behaviors. In order to analyze community structure with user trust relationship more appropriately in the large-scale multilevel social network environment, a novel local community detection model E-MLCD is proposed in this paper. It is jointly based on the multilevel properties and the strength of similarity of multilevel social interaction among communities. By studying three real-world multilevel social networks and specific QQ Zone marketing data, the model defines a new metric of community trust based on similarity. Comparison between other state-of-the-art detection methods demonstrate E-MLCD’s ability to detect communities more effectively.

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“…In the works of the authors [23], as a rule, there are methods using hard protection: password protection, cryptographic protection, as well as signature analysis and group detection. These methods are used in networks MANET, IoF, P2P [24], which are not so much limited in resources as groups of mobile robots.…”

Section: Implementation and Detection Of Ddos Attacksmentioning

confidence: 99%

Probabilistic Method for Anomalies Detection Based on the Analysis of Cyber Parameters in a Group of Mobile Robots

Basan¹,

Basan²,

Makarevich³

2018

Adv. sci. technol. eng. syst. j.

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This article is devoted to the issues of ensuring the security of a group of mobile robots in the implementation of attacks aimed at the property of accessibility of information and the availability of network nodes. The article presents a method for detecting an abnormal behavior of a network node based on the analysis by the group members of the parameters: residual energy and network load. Analysis of the behavior of individual robots relative to general behavior using probabilistic methods avoids the problem of creating a reference distribution for describing the behavior of a node, as well as creating a signature database for detecting anomalies. The developed method demonstrates high detection rate of denial of service attack and distributed denial of service attack with the number of malicious nodes not exceeding or slightly exceeding the number of trusted nodes. It also provides detection of the Sybil attack.

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Neighbor Similarity Trust against Sybil Attack in P2P E-Commerce

Cited by 35 publications

References 19 publications

From similarity perspective: a robust collaborative filtering approach for service recommendations

From similarity perspective: a robust collaborative filtering approach for service recommendations

Consumers Team Detection Model Based on Trust for Multi-Level

Probabilistic Method for Anomalies Detection Based on the Analysis of Cyber Parameters in a Group of Mobile Robots

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