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Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis.

Zhou W, Wen J, Koh YS, Xiong Q, Gao M, Dobbie G, Alam S - PLoS ONE (2015)

Bottom Line: Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach.In order to detect more complicated attack models, we propose a novel metric called DegSim' based on DegSim.The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science, Chongqing University, Chongqing, China.

ABSTRACT
Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim' based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks.

No MeSH data available.


RD-TIA Detecting structure based on two metrics RDMA and DegSim.There are two phases in RD-TIA. In the first phase, extract profile attributes and determine the suspicious profiles by using two statistical metrics DegSim and RDMA.
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pone.0130968.g003: RD-TIA Detecting structure based on two metrics RDMA and DegSim.There are two phases in RD-TIA. In the first phase, extract profile attributes and determine the suspicious profiles by using two statistical metrics DegSim and RDMA.

Mentions: There are two phases in RD-TIA. In the first phase, we extract profile attributes using Eqs (1) and (2), as shown in Fig 3; determine the suspicious profiles by using two statistical metrics, DegSim and RDMA. From this process, we get a pool of suspicious profiles SUSRD. The pseudocode is shown in Algorithm 1.


Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis.

Zhou W, Wen J, Koh YS, Xiong Q, Gao M, Dobbie G, Alam S - PLoS ONE (2015)

RD-TIA Detecting structure based on two metrics RDMA and DegSim.There are two phases in RD-TIA. In the first phase, extract profile attributes and determine the suspicious profiles by using two statistical metrics DegSim and RDMA.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4519300&req=5

pone.0130968.g003: RD-TIA Detecting structure based on two metrics RDMA and DegSim.There are two phases in RD-TIA. In the first phase, extract profile attributes and determine the suspicious profiles by using two statistical metrics DegSim and RDMA.
Mentions: There are two phases in RD-TIA. In the first phase, we extract profile attributes using Eqs (1) and (2), as shown in Fig 3; determine the suspicious profiles by using two statistical metrics, DegSim and RDMA. From this process, we get a pool of suspicious profiles SUSRD. The pseudocode is shown in Algorithm 1.

Bottom Line: Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach.In order to detect more complicated attack models, we propose a novel metric called DegSim' based on DegSim.The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science, Chongqing University, Chongqing, China.

ABSTRACT
Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim' based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks.

No MeSH data available.