| Title | A Shilling Attack Model Based on TextCNN |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Hu, Dongfang, Xu, Bin, Wang, Jun, Han, Linfeng, Liu, Jiayi |
| Conference Name | 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) |
| Keywords | Analytical models, Classification algorithms, Data models, detection algorithms, feature extraction, human factors, machine learning algorithms, pubcrawl, Random walking, recommender systems, Resiliency, Scalability, Shilling Attack, TextCNN, User rating data |
| Abstract | With the development of the Internet, the amount of information on the Internet is increasing rapidly, which makes it difficult for people to select the information they really want. A recommendation system is an effective way to solve this problem. Fake users can be injected by criminals to attack the recommendation system; therefore, accurate identification of fake users is a necessary feature of the recommendation system. Existing fake user detection algorithms focus on designing recognition methods for different types of attacks and have limited detection capabilities against unknown or hybrid attacks. The use of deep learning models can automate the extraction of false user scoring features, but neural network models are not applicable to discrete user scoring data. In this paper, random walking is used to rearrange the otherwise discrete user rating data into a rating feature matrix with spatial continuity. The rating data and the text data have some similarity in the distribution mode. By effective analogy, the TextCNN model originally used in NLP domain can be improved and applied to the classification task of rating feature matrix. Combining the ideas of random walking and word vector processing, this paper proposes a TextCNN detection model for user rating data. To verify the validity of the proposed model, the model is tested on MoiveLens dataset against 7 different attack detection algorithms, and exhibits better performance when compared with 4 attack detection algorithms. Especially for the Aop attack, the proposed model has nearly 100% detection performance with F1 - value as the evaluation index. |
| DOI | 10.1109/AUTEEE50969.2020.9315588 |
| Citation Key | hu_shilling_2020 |
A Shilling Attack Model Based on TextCNN