A novel item anomaly detection approach against shilling attacks in collaborative recommendation systems using the dynamic time interval segmentation technique

Xia, Hui; Fang, Bin; Gao, Min; Ma, Hui; Tang, Yuanyan; Wen, Jia

doi:10.1016/j.ins.2015.02.019

Cited by 59 publications

(30 citation statements)

References 51 publications

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“…The weight is 1,2 w α of this rule 0,108. for these rules. Iterative method implementation for a rule 10,11 , r β with the first priority allowed to increase the value 10,11 w ∆ to 0,385. The number of users -potential attackers at the same time decreased by more than 5 times.…”

Section: Resultsmentioning

confidence: 99%

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Method for Detecting Shilling Attacks in E-Commerce Systems Using Weighted Temporal Rules

Chala

Novikova

Chernyshova

2019

EUREKA: Physics and Engineering

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The problem of shilling attacks detecting in e-commerce systems is considered. The purpose of such attacks is to artificially change the rating of individual goods or services by users in order to increase their sales. A method for detecting shilling attacks based on a comparison of weighted temporal rules for the processes of selecting objects with explicit and implicit feedback from users is proposed. Implicit dependencies are specified through the purchase of goods and services. Explicit feedback is formed through the ratings of these products. The temporal rules are used to describe hidden relationships between the choices of user groups at two consecutive time intervals. The method includes the construction of temporal rules for explicit and implicit feedback, their comparison, as well as the formation of an ordered subset of temporal rules that capture potential shilling attacks. The method imposes restrictions on the input data on sales and ratings, which must be ordered by time or have timestamps. This method can be used in combination with other approaches to detecting shilling attacks. Integration of approaches allows to refine and supplement the existing attack patterns, taking into account the latest changes in user priorities.

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Section: Resultsmentioning

confidence: 99%

“…However, they do not distinguish between attackers and users with periodically changing interests, who occasionally purchase goods and services in e-commerce systems. In order to take into account the changing interests of users, a subset of the initial ratings is allocated for a given time interval [10].…”

Section: Introductionmentioning

confidence: 99%

Method for Detecting Shilling Attacks in E-Commerce Systems Using Weighted Temporal Rules

Chala

Novikova

Chernyshova

2019

EUREKA: Physics and Engineering

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“…There are also some scholars assigning users (products) to different clusters dynamically by evolutionary combined clustering approach, and make further recommendations [16]. Additionally, there are some other novel recommendation approaches [17,18,19,20,21,22].…”

Section: Related Workmentioning

confidence: 99%

A Personalized Time-Sequence-Based Book Recommendation Algorithm for Digital Libraries

Zhang

2016

IEEE Access

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Abstract-Book recommendations are of great significance in colleges and universities. Although current recommendation approaches have made significant achievements, these approaches do not consider college students' similar learning trajectories in the same major. In order to recommend books more accurately, mining the knowledge system is very crucial for college students in the same major. This paper proposes a personalized book recommendation algorithm that is based on the time sequential collaborative filtering recommendation, combined with students' learning trajectories. In order to recommend books effectively, our algorithm leverages space distance. In this algorithm, we consider two important characteristics: the time sequence information of borrowing books and the circulation times of books. Our experimental results demonstrate that our book recommendation algorithm is in accordance with the college students' demand for professional learning.Index Terms-time sequence information, collaborative filtering, book recommendation.

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“…For detection, supervised learning-based detection systems using several user features [9][10][11], unsupervised detection based on clustering algorithms [12,13], and semi-supervised detection using both labeled and unlabeled data [14,15] are available. For robust attack-resistant CF, trust-aware CF based on constructing a user trust model [16,17] and item anomaly detection-based robust CF [18] are available. However, recent research on the shilling attacks in service recommender systems is inadequate.…”

Section: Introductionmentioning

confidence: 99%

From similarity perspective: a robust collaborative filtering approach for service recommendations

Gao

Lü

Yang

et al. 2019

Front. Comput. Sci.

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Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions. We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.

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A novel item anomaly detection approach against shilling attacks in collaborative recommendation systems using the dynamic time interval segmentation technique

Cited by 59 publications

References 51 publications

Method for Detecting Shilling Attacks in E-Commerce Systems Using Weighted Temporal Rules

Method for Detecting Shilling Attacks in E-Commerce Systems Using Weighted Temporal Rules

A Personalized Time-Sequence-Based Book Recommendation Algorithm for Digital Libraries

From similarity perspective: a robust collaborative filtering approach for service recommendations

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