{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:09:24Z","timestamp":1787908164811,"version":"build-2784847793"},"reference-count":18,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/sym14102132","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T01:44:13Z","timestamp":1665711853000},"page":"2132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Intelligent Based Symmetrical Classification of Online Shop Selling Counterfeit Products"],"prefix":"10.3390","volume":"14","author":[{"given":"Shyh-Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science, Tunghai University, Taichung 407224, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po-Hsiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tunghai University, Taichung 407224, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ching-Tsorng","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tunghai University, Taichung 407224, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Hui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Chinese Culture University, Taipei 11114, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127","DOI":"10.2501\/JAR-54-2-127-132","article-title":"Pitfalls and fraud in online advertising metrics: What makes advertisers vulnerable to cheaters, and how they can protect themselves","volume":"54","author":"Edelman","year":"2014","journal-title":"J. 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