An Intelligent Based Symmetrical Classification of Online Shop Selling Counterfeit Products

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Title:	An Intelligent Based Symmetrical Classification of Online Shop Selling Counterfeit Products
Authors:	Chen, Shyh-Wei Chen, Po-Hsiang Tsai, Ching-Tsorng Liu, Chia-Hui
Contributors:	Department of Applied Mathematics
Keywords:	asymmetry information asymmetry information counterfeit website deep neural networks machine learning random forest
Date:	2022-10
Issue Date:	2022-11-22 15:09:28 (UTC+8)
Publisher:	MDPI
Abstract:	In recent years, the social network has become popular and people have started trading transactions on the Internet. Many counterfeit websites have begun to appear which create websites with counterfeit products or use the digital advertiser’s services to promote their websites on social media. Malicious sellers disguise high-quality products to attract consumers since buyers cannot receive transparent information. If there is asymmetry information, a secondary market will be formed. To solve the above problems, this research explored the machine-learning-based method to classify counterfeit and legitimate websites with symmetry information. The data set is 1612 websites used in this paper and a total of 15 feature values and takes 804 counterfeit websites and 808 legitimate websites. The Random Forest and Deep Neural Network algorithms were used to classify fake websites. This study also used statistical tests, such as Chi-square and ANOVA detection, to compare the importance of features in feature selection. The experiment results show that the RF accuracy is 99.2% and the DNN accuracy is 93.2%. The RF Precision and Recall are 100% and 98.5%, respectively. The DNN Precision and Recall are less than RF. Then, the RF F1-score is 99.2% which is higher than DNN.
Relation:	Symmetry 開放取用卷 14, 期 10 October 2022 論文號碼 2132
Appears in Collections:	[Department of Applied Mathematics] journal articles

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