Detection of Trust Shilling Attacks in Recommender Systems

被引：0

作者：

Chen X. ^{[1
]}

Deng X. ^{[2
]}

Huang C. ^{[2
]}

Shin H. ^{[1
]}

机构：

[1] Dept. of Computer Engineering, Konkuk University, Seoul

来源：

IEICE Transactions on Information and Systems | 2022年 / E105D卷 / 06期

基金：

新加坡国家研究基金会;

关键词：

recommender systems; shilling attacks detection; trust shilling attacks;

D O I：

10.1587/TRANSINF.2021EDL8094

中图分类号：

学科分类号：

摘要：

Most research on detecting shilling attacks focuses on users’ rating behavior but does not consider that attackers may also attack the users’ trusting behavior. For example, attackers may give a low score to other users’ ratings so that people would think the ratings from the users are not helpful. In this paper, we define the trust shilling attack, propose the behavior features of trust attacks, and present an effective detection method using machine learning methods. The experimental results demonstrate that, based on our proposed behavior features of trust attacks, we can detect trust shilling attacks as well as traditional shilling attacks accurately. Copyright © 2022 The Institute of Electronics, Information and Communication Engineers

引用

页码：1239 / 1242

页数：3

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