sis 18: e18

Research Article

Hybrid CNN and RNN-based shilling attack framework in social recommender networks

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  • @ARTICLE{10.4108/eai.2-11-2021.171754,
        author={Praveena Narayanan and Vivekanandan. K},
        title={Hybrid CNN and RNN-based shilling attack framework in social recommender networks},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={11},
        keywords={Recommender System, Shilling Attack, Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN), Interference Immunity},
        doi={10.4108/eai.2-11-2021.171754}
    }
    
  • Praveena Narayanan
    Vivekanandan. K
    Year: 2021
    Hybrid CNN and RNN-based shilling attack framework in social recommender networks
    SIS
    EAI
    DOI: 10.4108/eai.2-11-2021.171754
Praveena Narayanan1,*, Vivekanandan. K2
  • 1: Research Scholar, Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India
  • 2: Professor, Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India
*Contact email: praveenan@pec.edu

Abstract

INTRODUCTION: Recommender system is considered to be widely utilized in diversified domain for the purpose of effectively handling information overload. But, recommender systems are prone to vulnerabilities that are significantly exploited by malicious attacks. In particular, shilling attack is determined to crucial in the recommender system due to its openness characteristics and data dependence.

OBJECTIVES: Authors focused on detecting shilling attack by using hybrid deep learning techniques.

METHODS: Hybrid CNN and RNNs-based shilling attack framework is proposed for shilling attack detection based on the selection of dynamic features for attaining maximized detection accuracy.

RESULTS: The proposed CNN-RNNs-based shilling attack framework was determined to improve the recall with different filler size under Netflix dataset by 4.48% and 6.14%, better than the benchmarked HDLM and RMRA frameworks. The proposed CNN-RNNs-based shilling attack framework was determined to minimize the false positive rate by 4.82% and 5.94%, better than the benchmarked HDLM and RMRA frameworks.

CONCLUSION: This framework integrated user popularity and rating-based indicators in order to consider the deviations that happens, when the users select items. It also included information entropy for dynamically choosing the detection indicators in order to improve the reliability in attack detection. It was proposed with three different attack detection models that contextually handles different shilling attacks.