An Unsupervised Algorithm for Detecting Shilling Attacks on Recommender Systems

Li, Cong; Luo, Zhigang; Shi, Jinlong

doi:10.3724/sp.j.1004.2011.00160

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In general, the shilling profiles should be injected with diverse attack sizes and filler sizes to simulate various attack situations. However, the attack size should not be too large (usually below 20%), otherwise the shilling profiles would be easily detected and the attack cost would be raised [12, 38]. Similarly, to evade the detection, the filler size of shilling profiles should be same or similar to the filler sizes of genuine profiles [7, 8].…”

Section: Experimental Evaluationmentioning

confidence: 99%

Detecting shilling profiles in collaborative recommender systems via multidimensional profile temporal features

Hao

Zhang

2018

IET Information Security

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Section: Experimental Evaluationmentioning

confidence: 99%

Detecting shilling profiles in collaborative recommender systems via multidimensional profile temporal features

Hao

Zhang

2018

IET Information Security

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“…Then, they proposed a PCA-Var Select detection that can effectively detect multiple attack types. Li Cong et al [17] constructed a corresponding object function for genetic optimization through qualifying the group effect of attack profiles and combined it with Bayesian inference in the process of genetic optimization, which is an new unsupervised algorithm for detecting shilling attack -IBIGDA. To some extent, IBIGDA reduces the dependence on prior knowledge, but it still assumes that the number of attack profiles is less than genuine profiles and obtain higher precision with sacrificing recall.…”

Section: Introductionmentioning

confidence: 99%

Finding Shilling Attack in Recommender System based on Dynamic Feature Selection

Cao

Zhang

Fan³

et al. 2018

International Conferences on Software Engineering and Knowledge Engineering

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Recommender system is widely used as an important tool in various fields for effectively dealing with information overload, and collaborative filtering algorithm plays a vital role in the system. However, such system is highly vulnerable to malicious attacks, especially shilling attack because of data openness and independence. Therefore, detecting shilling attack has become an important issue to ensure the security of recommender system. Most of existing methods for detecting shilling attack are based on rating classification features and their limitation is that they are easily to be interfered by obfuscation techniques. Moreover, traditional detection algorithms can not handle multiple types of shilling attack flexibly. In order to solve these problems, in this paper, we propose an outlier degree shilling attack detection algorithm based on dynamic feature selection. By considering the differences of user choosing items and taking user popularity as a detection metric, as well as using information entropy to select detection metrics dynamically, a variety of shilling attack models can be dealt with flexibly. Experiments show that the algorithm has stronger detection performance and interference immunity in shilling attack detection.

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An Outlier Degree Shilling Attack Detection Algorithm Based on Dynamic Feature Selection

Cao

Zhang

Zheng

et al. 2019

Int. J. Soft. Eng. Knowl. Eng.

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Recommender system is widely used in various fields for dealing with information overload effectively, and collaborative filtering plays a vital role in the system. However, recommender system suffers from its vulnerabilities by malicious attacks significantly, especially, shilling attacks because of the open nature of recommender system and the dependence on data. Therefore, detecting shilling attack has become an important issue to ensure the security of recommender system. Most of the existing methods of detecting shilling attack are based on user ratings, and one limitation is that they are likely to be interfered by obfuscation techniques. Moreover, traditional detection algorithms cannot handle different types of shilling attacks flexibly. In order to solve the problems, we proposed an outlier degree shilling attack detection algorithm by using dynamic feature selection. Considering the differences when users choose items, we combined rating-based indicators with user popularity, and utilized the information entropy to select detection indicators dynamically. Therefore, a variety of shilling attack models can be dealt with flexibility in this way. The experiments show that the proposed algorithm can achieve better detection performance and interference immunity.

show abstract

An Unsupervised Algorithm for Detecting Shilling Attacks on Recommender Systems

Cited by 3 publications

References 5 publications

Detecting shilling profiles in collaborative recommender systems via multidimensional profile temporal features

Detecting shilling profiles in collaborative recommender systems via multidimensional profile temporal features

Finding Shilling Attack in Recommender System based on Dynamic Feature Selection

An Outlier Degree Shilling Attack Detection Algorithm Based on Dynamic Feature Selection

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