AndroClass: An Effective Method to Classify Android Applications by Applying Deep Neural Networks to Comprehensive Features
المؤلفون المشاركون
Reyhani Hamedani, Masoud
Shin, Dongjin
Lee, Myeonggeon
Cho, Seong-Je
Hwang, Changha
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-21، 21ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-09-10
دولة النشر
مصر
عدد الصفحات
21
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Android application (app) stores contain a huge number of apps, which are manually classified based on the apps’ descriptions into various categories.
However, the predefined categories or apps descriptions are usually not very accurate to reflect the real functionalities of apps, thereby leading to misclassify the apps, which may cause serious security issues and unreliability problem in the app store.
Therefore, the automatic app classification is an important demand to construct a secure, reliable, integrated, and easy to navigate app store.
In this paper, we propose an effective method called AndroClass to automatically classify apps based on their real functionalities by using rich and comprehensive features representing the actual functionalities of the apps.
AndroClass performs three steps of feature extraction, feature refinement, and classification.
In the feature extraction step, we extract 14 various features for each app by utilizing a unified tool suite.
In the feature refinement step, we apply Random Forest algorithm to refine the features.
In the classification step, we combine refined features into a single one and AndroClass is equipped with K-Nearest 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 stable and clearly represent the actual functionalities of the app, AndroClass does not pose any issues to the user privacy, and our method can be applied to classify unreleased or newly released apps.
The results of extensive experiments with two real-world datasets and a dataset constructed by human experts demonstrate the effectiveness of AndroClass where the classification accuracy of AndroClass with the latter dataset is 83.5%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Reyhani Hamedani, Masoud& Shin, Dongjin& Lee, Myeonggeon& Cho, Seong-Je& Hwang, Changha. 2018. AndroClass: An Effective Method to Classify Android Applications by Applying Deep Neural Networks to Comprehensive Features. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1215719
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Reyhani Hamedani, Masoud…[et al.]. AndroClass: An Effective Method to Classify Android Applications by Applying Deep Neural Networks to Comprehensive Features. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-21.
https://search.emarefa.net/detail/BIM-1215719
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Reyhani Hamedani, Masoud& Shin, Dongjin& Lee, Myeonggeon& Cho, Seong-Je& Hwang, Changha. AndroClass: An Effective Method to Classify Android Applications by Applying Deep Neural Networks to Comprehensive Features. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1215719
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1215719
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر