Research Article
Hybrid CNN and RNN-based shilling attack framework in social recommender networks
@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
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.
Copyright © 2021 Praveena Narayanan et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.