Robust Sybil attack defense with information level in online Recommender Systems

Noh, Giseop; Kang, Young-myoung; Oh, Hayoung; Kim, Chong-Kwon

doi:10.1016/j.eswa.2013.08.077

Cited by 24 publications

(7 citation statements)

References 6 publications

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“…Moreover, the shilling attack was built on the Sybil attack [45][46][47][48][49], which has been studied in the security literature. The researchers observed that the attacks demonstrate the capability to severely distort recommendation results [48,49]. To solve the problem, DSybil [48] and RobuRec [49] were proposed based on sufficient information and overwhelming condition.…”

Section: Related Workmentioning

confidence: 99%

“…The researchers observed that the attacks demonstrate the capability to severely distort recommendation results [48,49]. To solve the problem, DSybil [48] and RobuRec [49] were proposed based on sufficient information and overwhelming condition. RobuRec is suitable for the recommenders with general scoring systems, whereas Dsybil is suitable only for binary feedback systems.…”

Section: Related Workmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

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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“…However, Dsybil is not applicable to general RSs and nor can predict hidden user ratings since it only considers binary ranking (good or bad) systems. Our previous work in [30] eliminates the limitations of [29], which showed the possibility of using user information levels in general-purpose RSs. To mitigate the effects of Sybil attacks on RSs, the authors of [31] proposed a probabilistic latent semantic analysis (PLSA).…”

Section: Robust Rssmentioning

confidence: 99%

Toward trustworthy social network services: A robust design of recommender systems

Noh

Lee

et al. 2015

J. Commun. Netw.

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In recent years, electronic commerce and online social networks (OSNs) have experienced fast growth, and as a result, recommendation systems (RSs) have become extremely common. Accuracy and robustness are important performance indexes that characterize customized information or suggestions provided by RSs. However, nefarious users may be present, and they can distort information within the RSs by creating fake identities (Sybils). Although prior research has attempted to mitigate the negative impact of Sybils, the presence of these fake identities remains an unsolved problem. In this paper, we introduce a new weighted link analysis and influence level for RSs resistant to Sybil attacks. Our approach is validated through simulations of a broad range of attacks, and it is found to outperform other state-of-the-art recommendation methods in terms of both accuracy and robustness.

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“…The challenges of existing recommender systems mainly include cold start [12], [13], data sparsity [14], [15], and attacks [16], [17]. Trust has been regarded as one kind of commonly-used auxiliary information to help design more efficient recommender systems by solving these challenges to some extent.…”

Section: Related Workmentioning

confidence: 99%

Mining Implicit Correlations between Users with the Same Role for Trust-Aware Recommendation

Liu¹,

Yang²,

Zhang³

et al. 2015

KSII TIIS

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Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.

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Robust Sybil attack defense with information level in online Recommender Systems

Cited by 24 publications

References 6 publications

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

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

Toward trustworthy social network services: A robust design of recommender systems

Mining Implicit Correlations between Users with the Same Role for Trust-Aware Recommendation

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