METHOD FOR DETECTING SHILLING ATTACKS IN E-COMMERCE SYSTEMS USING WEIGHTED TEMPORAL RULES
| Autor: | Oksana Chala, Lyudmyla Novikova, Larysa Chernyshova |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry General Engineering General Physics and Astronomy feedback 02 engineering and technology E-commerce Recommender system computer.software_genre 020303 mechanical engineering & transports Goods and services recommendation system temporal rules 0203 mechanical engineering Order (business) User group 0202 electrical engineering electronic engineering information engineering e-commerce Attack patterns 020201 artificial intelligence & image processing Timestamp Data mining business shilling attack computer |
| Zdroj: | EUREKA: Physics and Engineering. 5:29-36 |
| ISSN: | 2461-4262 2461-4254 |
| DOI: | 10.21303/2461-4262.2019.00983 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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