Abstract
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.
Highlights
Recommendation systems are used by e-commerce companies to provide the best services to their customers by recommending items that they would like [1]
The most common attack features generally used for detection are Rating Deviation from Mean Agreement (RDMA) [10], Weighted Deviation from Mean Agreement (WDMA) [18], Weighted Deviation from Agreement (WDA) [18], Length Variance (LENVAR) [19], Degree of Similarity (DEGSIM) [18], Filler Mean Target Difference (FMTD) [18], Filler Mean Difference (FMD), Mean-Variance (MEANVAR) [18], and Target Model Focus (TMF) [18]
Let, Ru = top-N list and t = {t1, t2,.., tk} be the set of target items to be pushed, and U= set of users; for every item ti, hit ratio is calculated by Eq (1)
Summary
Recommendation systems are used by e-commerce companies to provide the best services to their customers by recommending items that they would like [1]. Appropriate recommendations have become a challenging task in the presence of overwhelmed data In such a scenario, the recommendation systems employ a variety of filtering techniques to retrieve useful information from a huge amount of data. Collaborative filtering is frequently used for recommending top-N items by considering the preferences of top-k similar users to the target user. In item-based collaborative filtering techniques, several item-based top-N recommendation algorithms are proposed, which utilize the rating information of users and similarity values between items [6]. The generated top-N lists by the predicted rating may be vulnerable in the presence of shilling attackers. A shilling attack involves inserting fake user profiles into a database to change the recommended top-N list of items [7]. The general objective of the attacker is to bias the overall top-N list as well as the top-N recommendation of a user
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