| Title | Detecting group shilling attacks in recommender systems based on maximum dense subtensor mining |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yu, Hongtao, Zheng, Haihong, Xu, Yishu, Ma, Ru, Gao, Dingli, Zhang, Fuzhi |
| Conference Name | 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) |
| Date Published | jun |
| Keywords | Conferences, Data models, dual-input convolutional neural network, feature extraction, Fuses, group shilling attacks, Human Behavior, Knowledge engineering, maximum dense subtensor mining, pubcrawl, recommender systems, resilience, Resiliency, Scalability, tensors, Time series analysis |
| Abstract | Existing group shilling attack detection methods mainly depend on human feature engineering to extract group attack behavior features, which requires a high knowledge cost. To address this problem, we propose a group shilling attack detection method based on maximum density subtensor mining. First, the rating time series of each item is divided into time windows and the item tensor groups are generated by establishing the user-rating-time window data models of three-dimensional tensor. Second, the M-Zoom model is applied to mine the maximum dense subtensor of each item, and the subtensor groups with high consistency of behaviors are selected as candidate groups. Finally, a dual-input convolutional neural network model is designed to automatically extract features for the classification of real users and group attack users. The experimental results on the Amazon and Netflix datasets show the effectiveness of the proposed method. |
| DOI | 10.1109/ICAICA52286.2021.9498095 |
| Citation Key | yu_detecting_2021 |
Detecting group shilling attacks in recommender systems based on maximum dense subtensor mining