UD-HMM: An unsupervised method for shilling attack detection based on hidden Markov model and hierarchical clustering

UD-HMM: An unsupervised method for shilling attack detection based on hidden Markov model and... The existing unsupervised methods usually require a prior knowledge to ensure the performance when detecting shilling attacks in collaborative filtering recommender systems. To address this limitation, in this paper we propose an unsupervised method to detect shilling attacks based on hidden Markov model and hierarchical clustering. We first use hidden Markov model to model user's history rating behaviors and calculate each user's suspicious degree by analyzing the user's preference sequence and the difference between genuine and attack users in rating behaviors. Then we use the hierarchical clustering method to group users according to user's suspicious degree and obtain the set of attack users. The experimental results on the MovieLens 1 M and Netflix datasets show that the proposed method outperforms the baseline methods in detection performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge-Based Systems Elsevier

UD-HMM: An unsupervised method for shilling attack detection based on hidden Markov model and hierarchical clustering

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0950-7051
D.O.I.
10.1016/j.knosys.2018.02.032
Publisher site
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Abstract

The existing unsupervised methods usually require a prior knowledge to ensure the performance when detecting shilling attacks in collaborative filtering recommender systems. To address this limitation, in this paper we propose an unsupervised method to detect shilling attacks based on hidden Markov model and hierarchical clustering. We first use hidden Markov model to model user's history rating behaviors and calculate each user's suspicious degree by analyzing the user's preference sequence and the difference between genuine and attack users in rating behaviors. Then we use the hierarchical clustering method to group users according to user's suspicious degree and obtain the set of attack users. The experimental results on the MovieLens 1 M and Netflix datasets show that the proposed method outperforms the baseline methods in detection performance.

Journal

Knowledge-Based SystemsElsevier

Published: May 15, 2018

References

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