Generating A New Shilling Attack for Recommendation Systems  

作　　者：Pradeep Kumar Singh Pijush Kanti Dutta Pramanik Madhumita Sardar Anand Nayyar Mehedi Masud Prasenjit Choudhury 

机构地区：[1]Department of Computer Science&Engineering,National Institute of Technology,Durgapur,India [2]Graduate School,Duy Tan University,Da Nang,Vietnam [3]Faculty of Information Technology,Duy Tan University,Da Nang,Vietnam [4]Department of Computer Science,College of Computers and Information Technology,Taif University,Taif,21944,Saudi Arabia

出　　处：《Computers, Materials & Continua》2022年第5期2827-2846,共20页计算机、材料和连续体（英文）

基　　金：Funding is provided by Taif University Researchers Supporting Project number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.

摘　　要：A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing.To keep the recommendation systems reliable,authentic,and superior,the security of these systems is very crucial.Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks,in this paper,we prove that they fail to detect a new or unknown attack.We develop a new attack model,named Obscure attack,with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended.The Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the list.Our proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing attacks.The effectivity of the proposed attack model is tested on the MovieLens dataset,where various classifiers like SVM,J48,random forest,and naïve Bayes are utilized.

关 键 词：Shilling attack recommendation system collaborative filtering top-N recommendation BIASING SHUFFLING hit ratio 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]