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Unsupervised and supervised methods for the detection of hurriedly created profiles in recommender systems

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Abstract

Recommender systems try to provide users with accurate personalized suggestions for items based on an analysis of previous user decisions and the decisions made by other users. These systems suffer from profile injection attacks, where malicious profiles are generated in order to promote or demote a particular item introducing abnormal ratings. The problem of automatic detection of such malicious profiles has been recently addressed by a great number of authors in the literature using supervised and unsupervised approaches. In this paper, we propose a framework to identify anomalous rating profiles, where each attacker (outlier) hurriedly creates profiles that inject into the system an unspecified combination of random ratings and specific ratings, without any prior knowledge of the existing ratings. This attack is a superset of the two different attacks (Uniform and Delta) proposed in Harper et al. (ACM Trans Interact Intell Syst 5(4):19, 2016) making the attack model more realistic and its detection more challenging. The proposed detection method is based on several attributes related to the unpredictable behavior of the outliers in a validation set, on the user-item rating matrix, on the similarity between users and on the filler items. In this work, we propose a new attribute (RIS) to capture the randomness in item selection of the abnormal profiles. In this work, three different systems are proposed: (1) a probabilistic framework that estimates the probability of a user to be an outlier by combining several features in a completely unsupervised way. (2) An unsupervised clustering system based on the k-means algorithm that automatically spots the spurious profiles. (3) A supervised framework that uses a random forest classifier for cases where labeling sample data is available. Experimental results on the MovieLens and the Small Netflix datasets demonstrate the high performance of the proposed methods as well as the discrimination accuracy of the proposed features.

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Notes

  1. On the opposite case, e.g. under RIS attribute, the \(1-CDF\) can be used.

  2. \(\overline{w}(f) = \frac{w(f)}{\sum _{f'} w(f')}\)

  3. The code implementing the proposed method together with the datasets and the experimental results is publicly available at https://sites.google.com/site/costaspanagiotakis/research/hurryattackrs.

  4. The rest of the features proposed in [13] were not computed, since they use connections between users that are not available in our datasets.

  5. https://grouplens.org/datasets/movielens/100k/

  6. https://grouplens.org/datasets/movielens/1m/

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Acknowledgements

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (Project Code: T1EDK-02147).

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Authors and Affiliations

  1. Department of Management Science and Technology, Hellenic Mediterranean University, 72100, Agios Nikolaos, Crete, Greece

    Costas Panagiotakis

  2. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004, Heraklion, Crete, Greece

    Harris Papadakis & Paraskevi Fragopoulou

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  1. Costas Panagiotakis
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  3. Paraskevi Fragopoulou
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Correspondence to Costas Panagiotakis.

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Panagiotakis, C., Papadakis, H. & Fragopoulou, P. Unsupervised and supervised methods for the detection of hurriedly created profiles in recommender systems. Int. J. Mach. Learn. & Cyber. 11, 2165–2179 (2020). https://doi.org/10.1007/s13042-020-01108-4

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