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However, the predefined categories or apps descriptions are usually<jats:italic>not<\/jats:italic>very accurate to reflect the real functionalities of apps, thereby leading to<jats:italic>misclassify<\/jats:italic>the apps, which may cause serious<jats:italic>security issues<\/jats:italic>and<jats:italic>unreliability<\/jats:italic>problem in the app store. Therefore, the automatic app classification is an<jats:italic>important<\/jats:italic>demand to construct a<jats:italic>secure<\/jats:italic>,<jats:italic>reliable<\/jats:italic>,<jats:italic>integrated<\/jats:italic>, and<jats:italic>easy to navigate<\/jats:italic>app store. In this paper, we propose an effective method called<jats:italic>AndroClass<\/jats:italic>to<jats:italic>automatically<\/jats:italic>classify apps based on their<jats:italic>real<\/jats:italic>functionalities by using<jats:italic>rich<\/jats:italic>and<jats:italic>comprehensive<\/jats:italic>features representing the<jats:italic>actual<\/jats:italic>functionalities of the apps. AndroClass performs<jats:italic>three<\/jats:italic>steps of<jats:italic>feature extraction<\/jats:italic>,<jats:italic>feature refinement<\/jats:italic>, and<jats:italic>classification<\/jats:italic>. In the feature extraction step, we extract 14 various features for each app by utilizing a<jats:italic>unified tool suite<\/jats:italic>. In the feature refinement step, we apply<jats:italic>Random Forest<\/jats:italic>algorithm to refine the features. In the classification step, we combine refined features into a<jats:italic>single<\/jats:italic>one and AndroClass is equipped with K\u2010Nearest Neighbor, Naive Bayes, Support Vector Machine, and Deep Neural Network to classify apps. On the contrary to the existing methods, all the utilized features in AndroClass are<jats:italic>stable<\/jats:italic>and<jats:italic>clearly<\/jats:italic>represent the actual functionalities of the app, AndroClass does<jats:italic>not<\/jats:italic>pose any issues to the<jats:italic>user privacy<\/jats:italic>, and our method can be applied to classify<jats:italic>unreleased<\/jats:italic>or<jats:italic>newly released<\/jats:italic>apps. The results of<jats:italic>extensive<\/jats:italic>experiments with two<jats:italic>real-world<\/jats:italic>datasets and a dataset constructed by<jats:italic>human experts<\/jats:italic>demonstrate the effectiveness of AndroClass where the classification accuracy of AndroClass with the latter dataset is 83.5%.<\/jats:p>","DOI":"10.1155\/2018\/1250359","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T23:30:28Z","timestamp":1536622228000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["AndroClass: An Effective Method to Classify Android Applications by Applying Deep Neural Networks to Comprehensive Features"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1529-5473","authenticated-orcid":false,"given":"Masoud","family":"Reyhani Hamedani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7182-4646","authenticated-orcid":false,"given":"Dongjin","family":"Shin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4555-6558","authenticated-orcid":false,"given":"Myeonggeon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9917-0429","authenticated-orcid":false,"given":"Seong-Je","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1262-5051","authenticated-orcid":false,"given":"Changha","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,9,10]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diin.2015.02.001"},{"key":"e_1_2_9_2_2","doi-asserted-by":"crossref","unstructured":"LeeS. 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