Shilling Attacks Detection in Collaborative Recommender System: Challenges and Promise

Zayed, Reda A.; Ibrahim, Lamiaa Fattouh; Hefny, Hesham A.; Salman, Hesham A.

doi:10.1007/978-3-030-44038-1_39

Cited by 11 publications

(9 citation statements)

References 22 publications

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“…Various methods have been used for this purpose, such as statistical methods [29], graph mining [26], clustering [30], classification [31] and deep learning [32][33] [24]. One general categorization of these methods is based on memory or model-based [34].…”

Section: Attack Detection Methodsmentioning

confidence: 99%

“…Because it follows from a comparison between the target user and other users based solely on the accumulation of ratings, a user with few ratings becomes difficult to categorize [5]. Generally, CF techniques are divided into two types, model-based filtering or memory-based filtering techniques [11]. Model-based techniques include clustering techniques, association techniques, Bayesian networks and neural networks.…”

Section: Collaborative Filteringmentioning

confidence: 99%

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Efficient Detection of Shillings Attacks in Collaborative Filtering Recommendation Systems using Hybrid Deep Learning: A CNN-based Model and Architecture

Ebrahimian

2024

Preprint

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<p>Recommendation systems are widely used in various areas to personalize recommendations to users. However, they are vulnerable to shilling attacks in which malicious users try to promote their products or diminish their competitors. Therefore, detecting shilling attacks can significantly improve the quality of recommender systems. With the increasing complexity of attacks and changes in attackers' behaviour, more advanced approaches are required to find the hidden patterns in data. This thesis proposes a CNN-based hybrid model and architecture that integrates self-learning and flexible aspects of CNN with other supervised learning methods to enhance shillings attacks detection on collaborative filtering recommendation systems. We also introduce a new metric, the F-compatible score, to measure the compatibility ratio of merging the CNN with any other classifier in a given aggregate. This measure helps in monitoring the enhancement level of the detection results of shilling attacks. Two different approaches are proposed in this thesis, user-based, and item-based. The experimental analysis used three benchmark datasets: the Movie-Lens 100K, Netflix, and Movie-Lens 1M. A comprehensive comparative analysis is also completed to assess the performance of both individual-based and hybrid-based detection models. Experimental results show that the proposed hybrid models performed well on most attack profiles and reached an F1-score of up to 99%. We concluded that the superiority of hybrid models over individual models depends on the sparsity level of data and the divergence of results.</p>

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Section: Attack Detection Methodsmentioning

confidence: 99%

Section: Collaborative Filteringmentioning

confidence: 99%

Efficient Detection of Shillings Attacks in Collaborative Filtering Recommendation Systems using Hybrid Deep Learning: A CNN-based Model and Architecture

Ebrahimian

2024

Preprint

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“…However, 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.…”

Section: Introductionmentioning

confidence: 99%

Generating A New Shilling Attack for Recommendation Systems

Singh¹,

Pramanik²,

Sardar³

et al. 2022

Computers, Materials &Amp; Continua

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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.

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“…In [30] and [22], the discussions do not consider the different detection attributes used in supervised and unsupervised detection methods. Both [24] and [25] briefly discuss the various attack and detection methods. There is no discussion on robust algorithms, and the detection methods are not categorized.…”

Section: Introductionmentioning

confidence: 99%

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

et al. 2020

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The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online vendors to provide recommendations to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. Over the years, multiple attack models and detection techniques have been developed to mitigate the problem. This paper aims to be a comprehensive survey of the shilling attack models, detection attributes, and detection algorithms. Additionally, we unravel and classify the intrinsic traits of the injected profiles that are exploited by the detection algorithms, which has not been explored in previous works. We also briefly discuss recent works in the development of robust algorithms that alleviate the impact of shilling attacks, attacks on multi-criteria systems, and intrinsic feedback based collaborative filtering methods.

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Shilling Attacks Detection in Collaborative Recommender System: Challenges and Promise

Cited by 11 publications

References 22 publications

Efficient Detection of Shillings Attacks in Collaborative Filtering Recommendation Systems using Hybrid Deep Learning: A CNN-based Model and Architecture

Efficient Detection of Shillings Attacks in Collaborative Filtering Recommendation Systems using Hybrid Deep Learning: A CNN-based Model and Architecture

Generating A New Shilling Attack for Recommendation Systems

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

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