摘要
With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation Algorithm (GSAGenl) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGenl, a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD,SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM.
With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation Algorithm(GSAGenl) has outstanding performance. It can be immune to some PCC(Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGenl, a gravitation-based detection model(GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, Un RAP, AP-Un RAP Semi-SAD,SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM.
基金
supported by the National Natural Science Foundation of P.R.China(No.61672297)
the Key Research and Development Program of Jiangsu Province(Social Development Program,No.BE2017742)
The Sixth Talent Peaks Project of Jiangsu Province(No.DZXX-017)
Jiangsu Natural Science Foundation for Excellent Young Scholar(No.BK20160089)
作者简介
Lingtao Qi,is currently with successive postgraduate and doctoral programs of study with the School of Software Engineering,Nanjing University of Posts and Telecommunications.He received a B.Eng.degree in Information Security from Nanjing University of Posts and Telecommunications.His current research is in recommender systems and data security.Email:851388561@qq.com;corresponding author:Haiping Huang,email:hhp@njupt.edu.cn,is a professor and a Ph.D supervisor with the School of Computer Science and Technology,Nanjing University of Posts and Telecommunications,an associate editor of International Journal of Communication Systems and an editor of International Journal of Distributed Sensor Networks.He received the B.Eng.degree and M.Eng.degree from Nanjing University of Posts and Telecommunications and the Ph.D.degree in Computer Application Technology from Soochow Univer sity.His research interests include information security and privacy protection of wireless sensor networks.Email:hhp@njupt.edu.cn;Feng Li,received a B.Eng.degree in Computer Communication and M.Eng.Degree in Software Engineering from Nanjing University of Posts and Telecommunications.His research interests include collaborative filtering,shilling attacks and data security.Email:836402836@qq.com;Reza Malekian,is with the Department of Computer Science and Media Technology at Malm?University,Sweden and also an Extraordinary Professor with the Department of Electrical,Electronic,and Computer Engineering,University of Pretoria,South Africa.His current research interests include advanced sensor networks,Internet of Things,and mobile communications.Email:reza.malekian@ieee.org;Ruchuan Wang,is a professor and a Ph.D supervisor with the School of Computer Science and Technology,Nanjing University of Posts and Telecommunications,Nanjing,China.He received his B.S.degree in Computational Mathematics from The PLA Information Engineering University,Zhengzhou,China.He was a Visiting Scholar with Bremen University,Germany,Munich University,Germany,and Max-Planck Institute,Germany,during 1984-1992.His research interests include intelligent agent,information security,wireless networking and distributed computing.Email:wangrc@njupt.edu.cn.
