Affiliation:
1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Abstract
The recommendation algorithm based on collaborative filtering is vulnerable to data poisoning attacks, wherein attackers can manipulate system output by injecting a large volume of fake rating data. To address this issue, it is essential to investigate methods for detecting systematically injected poisoning data within the rating matrix. Since attackers often inject a significant quantity of poisoning data in a short period to achieve their desired impact, these data may exhibit spatial proximity. In other words, poisoning data may be concentrated in adjacent rows of the rating matrix. This paper capitalizes on the proximity characteristics of poisoning data in the rating matrix and introduces a sampling-based method for detecting data poisoning attacks. First, we designed a rating matrix sampling method specifically for detecting poisoning data. By sampling differences obtained from the original rating matrix, it is possible to infer the presence of poisoning attacks and effectively discard poisoning data. Second, we developed a method for pinpointing malicious data based on the distance of rating vectors. Through distance calculations, we can accurately identify the positions of malicious data. After that, we validated the method on three real-world datasets. The results demonstrate the effectiveness of our method in identifying malicious data within the rating matrix.
Funder
National Key Research and Development Plan
National Natural Science Foundation of China
Consulting project of the Chinese Academy of Engineering
Guangdong Basic and Applied Basic Research Foundation
“National Undergraduate Innovation and Entrepreneurship Training Program” at Guangzhou University
Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme
Guangdong Higher Education Innovation Group
Guangzhou Higher Education Innovation Group
Cultivation Project of PZL
Project of Guangzhou University
Guangzhou Basic and Applied Basic Research Foundation
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)