A State of Art Survey on Shilling Attack in Collaborative Filtering Based Recommendation System

Patel, Krupa; Thakkar, Amit; Shah, Chandni; Makvana, Kamlesh

doi:10.1007/978-3-319-30933-0_38

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…Labeled data is required to perform supervised learning and it is not usual to have it, so current research aims towards unsupervised learning detectors. [27] first make use of generic statistical attributes (RDMA, DegSim and LengthVar) to identify fake profiles from genuine ones, then they use the obtained values to train a supervised model for detection of malicious profiles.…”

Section: Related Workmentioning

confidence: 99%

Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

et al. 2019

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As the use of recommender systems becomes generalized in society, the interest in varying the orientation of their recommendations is increasing. There are shilling attacks' strategies that introduce malicious profiles in collaborative filtering recommender systems in order to promote the own products or services or to discredit those of the competition. Academic research against shilling attacks has been focused in statistical approaches to detect the unusual patterns in user ratings. Nowadays, there is a growing research area focused on the design of robust machine learning methods to neutralize the malicious profiles inserted into the system. This paper proposes an innovative robust method, based on matrix factorization, to neutralize the shilling attacks. Our method obtains the reliability value associated with each prediction of a user to an item. By monitoring the unusual reliability variations in the items prediction, we can avoid promoting the shilling predictions to the erroneous recommendations. This paper openly provides more than 13 000 individual experiments involving a wide range of attack strategies, both push, and nuke, in order to test the proposed approach. The results show that the proposed method is able to neutralize most of the existing attacks; its performance only decreases in the not relevant situations: when the attack size is not large enough to effectively affect the recommendations provided by the system.

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Section: Related Workmentioning

confidence: 99%

Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

et al. 2019

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“…The described attack represents only one possible attack to RSs. The profile injection attack (also known as the shilling attack) is undoubtedly the most discussed type of attack in literature [23,29,38]. As the name suggests, the profile injection attack seeks to mislead the RS by injecting well-crafted fake users into the system.…”

Section: Introductionmentioning

confidence: 99%

On the feasibility of crawling-based attacks against recommender systems1

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Picek

et al. 2022

JCS

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Nowadays, online services, like e-commerce or streaming services, provide a personalized user experience through recommender systems. Recommender systems are built upon a vast amount of data about users/items acquired by the services. Such knowledge represents an invaluable resource. However, commonly, part of this knowledge is public and can be easily accessed via the Internet. Unfortunately, that same knowledge can be leveraged by competitors or malicious users. The literature offers a large number of works concerning attacks on recommender systems, but most of them assume that the attacker can easily access the full rating matrix. In practice, this is never the case. The only way to access the rating matrix is by gathering the ratings (e.g., reviews) by crawling the service’s website. Crawling a website has a cost in terms of time and resources. What is more, the targeted website can employ defensive measures to detect automatic scraping. In this paper, we assess the impact of a series of attacks on recommender systems. Our analysis aims to set up the most realistic scenarios considering both the possibilities and the potential attacker’s limitations. In particular, we assess the impact of different crawling approaches when attacking a recommendation service. From the collected information, we mount various profile injection attacks. We measure the value of the collected knowledge through the identification of the most similar user/item. Our empirical results show that while crawling can indeed bring knowledge to the attacker (up to 65% of neighborhood reconstruction on a mid-size dataset and up to 90% on a small-size dataset), this will not be enough to mount a successful shilling attack in practice.

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“…In 2014, [28] produced one of the most comprehensive surveys on the topic, but it presents details on the attacks only until 2011. The survey in [23] focuses only on the statistical measures used in the detection and the basic shilling attack methods. Kaur et al [29] perform experimental evaluation comparing the most commonly used shilling attack methods.…”

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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A State of Art Survey on Shilling Attack in Collaborative Filtering Based Recommendation System

Cited by 13 publications

References 8 publications

Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

On the feasibility of crawling-based attacks against recommender systems1

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

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