DETECTION METHOD FOR SHORT-TERM SHILLING ATTACKS IN E-COMMERCE SYSTEMS USING ADAPTIVE GRANULARITY OF USER FEEDBACK
DOI:
https://doi.org/10.20998/2079-0023.2025.02.20Keywords:
shilling attacks, e-commerce systems, temporal rules, adaptive granularity, sales variability, attack detection, recommender systems, outliersAbstract
The subject of research is the process of detecting short-term shilling attacks in e-commerce systems based on analysis of temporal dependencies between explicit and implicit user feedback. The aim of the work is to develop an approach to detecting shilling attacks using temporal rules and adaptive granularity of both sales and ratings in e-commerce systems. Research tasks include: development of an approach to detecting short-term shilling attacks based on adaptive comparison of temporal rules for sales and ratings; development of a method for detecting short-term shilling attacks based on adaptive granularity of explicit and implicit user feedback. Explicit user feedback is represented by ratings, while implicit feedback is captured through product sales. The developed method includes the following stages: preliminary aggregation of sales data; analysis of sales and ratings variability through the ratio of standard deviation to mean value; identification of intervals with potential attack possibility; formation of a fact set with different granularity levels; construction of temporal rules of two types for sales and ratings; detection of shilling attack intervals based on comparison of rule weight signs for sales and ratings; identification of attacking users based on analysis of user activity across detected shilling attack intervals. The method provides automated selection of time granularity for determining sales facts and forming ratings and thereby improves the accuracy of detecting short-term attacks compared to fixed granularity, as well as enables attack detection in near-online mode. The practical significance of the obtained results lies in the possibility of detecting short-term rating distortions in e-commerce systems, social networks, and recommender systems to increase user trust in recommended products and services.
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