电子学报2012，Vol.40Issue(8)：1687-1693,7.DOI:10.3969/j.issn.0372-2112.2012.08.031
基于特征选择的推荐系统托攻击检测算法
Shilling Attack Detection Based on Feature Selection for Recommendation Systems
摘要
Abstract
Most of the e-business recommender systems are based upon collaborative filtering (CF) algorithms. Since such systems have been shown to be vulnerable to shilling attacks in which malicious user profiles ate inserted into the system in order to push or nuke the predictions of some targeted items, shilling attack detection has recently become a hot research topic in recommender systems. Firstly, the effectiveness of five types of attacks against different CF algorithms is analyzed. Secondly, a feature selection algorithm is presented. Two kinds of shilling attack detection algorithms based on supervised learning are then proposed: the first one is based on naive Bayesian classifier,and the second one is based on k nearest neighbor (κNN) classifier. At last,experimental results show the effectiveness of the feature selection algorithm and the sensitivity and specificity of these two kinds of detection algorithms.关键词
推荐系统/托攻击检测/特征选择/朴素贝叶斯分类/k近邻分类Key words
recommender system/ shilling attack detection/feature selection/naive Bayesian classifier/ JfcNN classifier分类
信息技术与安全科学引用本文复制引用
伍之昂,庄毅,王有权,曹杰..基于特征选择的推荐系统托攻击检测算法[J].电子学报,2012,40(8):1687-1693,7.基金项目
国家自然科学基金(No.61103229,No.71072172,No.61003074) （No.61103229,No.71072172,No.61003074）
浙江省自然科学基金(No.Z110822,No.Y1110644,No.Y1110969,No.Y1090165) （No.Z110822,No.Y1110644,No.Y1110969,No.Y1090165）
江苏省科技支撑计划工业部分(No.BE2011198) （No.BE2011198）
江苏省高等学校优秀科技创新团队(No.2011013) （No.2011013）
东南大学江苏省网络与信息安全重点实验室开放课题(No.BM2003201) （No.BM2003201）
江苏省高校科研成果产业化推进项目(No.JHB2011-21) （No.JHB2011-21）
