全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
电子学报  2012 

基于特征选择的推荐系统托攻击检测算法

DOI: 10.3969/j.issn.0372-2112.2012.08.031, PP. 1687-1693

Keywords: 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

Full-Text   Cite this paper   Add to My Lib

Abstract:

基于协同过滤的电子商务推荐系统极易受到托攻击,托攻击者注入伪造的用户模型增加或减少目标对象的推荐频率,如何检测托攻击是目前推荐系统领域的热点研究课题.分析五种类型托攻击对不同协同过滤算法产生的危害性,提出一种特征选择算法,为不同类型托攻击选取有效的检测指标.基于选择出的指标,提出两种基于监督学习的托攻击检测算法,第一种算法基于朴素贝叶斯分类;第二种算法基于k近邻分类.最后,通过实验验证了特征选择算法的有效性,及两种算法的灵敏性和特效性.

References

[1]  SK Lam,J Riedl.Shilling recommender systems for fun and profit.Proceedings of the 13th World Wide Web (WWW''04).New York,USA,2004,393-402.
[2]  N Hurley,Z Cheng,M Zhang.Statistical attack detection.Proceedings of ACM Conference on Recommender Systems (RecSys ''09).New York,USA,2009,149-156.
[3]  C Williams.Profile Injection Attack Detection for Securing Collaborative Recommender Systems.DePaul University CTI Technical Report,2006.
[4]  张锋,孙雪冬,常会友,赵淦森.两方参与的隐私保护协同过滤推荐研究[J].电子学报,2009,37(1):84-89. F Zhang,X Sun,H Chang,G Zhao.Research on privacy-preserving two-party collaborative filtering recommendation[J].Acta Electronic Sinica,2009,37(1):84-89.(in Chinese)
[5]  S Zhang,A Chakrabarti,J Ford,F Makedon.Attack detection in time series for recommender systems.Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD''06).Philadelphia,Pennsylvania,USA,2006.
[6]  P A Chirita,W.Nejdl,C Zamfir.Preventing shilling attacks in online recommender systems.Proceedings of ACM Int.Workshop on Web Information and Data Management.ACM Press,New York,NY,USA,2005.67-74.
[7]  R Burke,B Mobasher,C Williams R Bhaumik.Classification features for attack detection in collaborative recommendation systems.Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD''06).Philadelphia,Pennsylvania,USA,2006,542-547.
[8]  B Mehta,W Nejdl.Unsupervised strategies for shilling detection and robust collaborative filtering [J].User Modeling and User-Adapted Interaction,2009,19(1):65-97.
[9]  H Liu,L Yu.Toward integrating feature selection algorithms for classification and clustering [J].IEEE Transactions on Knowledge and Data Engineering,2005,17(4):491-502.
[10]  蒋盛益,郑琪,张倩生.基于聚类的特征选择方法[J].电子学报,2008,37(12):157-160. S Jiang,Q Zheng,Q Zhang.Clustering-based feature selection[J].Acta Electronica Sinica,2008,37(12):157-160.(in Chinese)
[11]  J L Herlocker,J A Konstan,LG Terveen,JT Riedl.Evaluating collaborative filtering recommender systems[J].ACM Transaction on Information Systems,2004,22(1):5-53.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133