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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.


RDMA and DegSim value distribution with average attacks.RDMA Metric value and DegSim Metric value of each profile of MovieLens 100k Dataset.
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pone.0130968.g001: RDMA and DegSim value distribution with average attacks.RDMA Metric value and DegSim Metric value of each profile of MovieLens 100k Dataset.

Mentions: The DegSim attribute is based on the average Pearson correlation of the profile’s k nearest neighbours and is calculated as follows:DegSim=∑u=1kWuvk(2)where Wuv is the Pearson correlation between user u and user v. In general the value of k can be easily determined for most datasets, as we can measure the degree of separability using different separable extension techniques used in computational geometry. Fig 1 shows the RDMA and DegSim value distribution in the random attack model, with an attack size of 20, filler size is 5%, and k = 20 in DegSim.


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)

RDMA and DegSim value distribution with average attacks.RDMA Metric value and DegSim Metric value of each profile of MovieLens 100k Dataset.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130968.g001: RDMA and DegSim value distribution with average attacks.RDMA Metric value and DegSim Metric value of each profile of MovieLens 100k Dataset.
Mentions: The DegSim attribute is based on the average Pearson correlation of the profile’s k nearest neighbours and is calculated as follows:DegSim=∑u=1kWuvk(2)where Wuv is the Pearson correlation between user u and user v. In general the value of k can be easily determined for most datasets, as we can measure the degree of separability using different separable extension techniques used in computational geometry. Fig 1 shows the RDMA and DegSim value distribution in the random attack model, with an attack size of 20, filler size is 5%, and k = 20 in DegSim.

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.