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題名 Clustering iOS executable using self-organizing maps
作者 Yu, F.;Huang, S.-Y.;Chiou, L.-C.;Tsaih, R.-H.
蔡瑞煌;郁方
貢獻者 資管系
關鍵詞 App stores; Data preprocessing; Implicit systems; Large dimensions; Mobile apps; Static binary analysis; System methods; Traditional clustering; Neural networks; Conformal mapping
日期 2013
上傳時間 16-Apr-2015 17:36:38 (UTC+8)
摘要 We pioneer the study on applying both SOMs and GHSOMs to cluster mobile apps based on their behaviors, showing that the SOM family works well for clustering samples with more than ten thousands of attributes. The behaviors of apps are characterized by system method calls that are embedded in their executable, but may not be perceived by users. In the data preprocessing stage, we propose a novel static binary analysis to resolve and count implicit system method calls of iOS executable. Since an app can make thousands of system method calls, it is needed a large dimension of attributes to model their behaviors faithfully. On collecting 115 apps directly downloaded from Apple app store, the analysis result shows that each app sample is represented with 18000+ kinds of methods as their attributes. Theoretically, such a sample representation with more than ten thousand attributes raises a challenge to traditional clustering mechanisms. However, our experimental result shows that apps that have similar behaviors (due to having been developed from the same company or providing similar services) can be clustered together via both SOMs and GHSOMs. © 2013 IEEE.
關聯 Proceedings of the International Joint Conference on Neural Networks,2013, 論文編號 6706728, Dallas, TX; United States; 4 August 2013 到 9 August 2013; 類別編號CFP13IJS-ART; 代碼 102436
10.1109/IJCNN.2013.6706728
資料類型 conference
dc.contributor 資管系
dc.creator (作者) Yu, F.;Huang, S.-Y.;Chiou, L.-C.;Tsaih, R.-H.
dc.creator (作者) 蔡瑞煌;郁方zh_TW
dc.date (日期) 2013
dc.date.accessioned 16-Apr-2015 17:36:38 (UTC+8)-
dc.date.available 16-Apr-2015 17:36:38 (UTC+8)-
dc.date.issued (上傳時間) 16-Apr-2015 17:36:38 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/74638-
dc.description.abstract (摘要) We pioneer the study on applying both SOMs and GHSOMs to cluster mobile apps based on their behaviors, showing that the SOM family works well for clustering samples with more than ten thousands of attributes. The behaviors of apps are characterized by system method calls that are embedded in their executable, but may not be perceived by users. In the data preprocessing stage, we propose a novel static binary analysis to resolve and count implicit system method calls of iOS executable. Since an app can make thousands of system method calls, it is needed a large dimension of attributes to model their behaviors faithfully. On collecting 115 apps directly downloaded from Apple app store, the analysis result shows that each app sample is represented with 18000+ kinds of methods as their attributes. Theoretically, such a sample representation with more than ten thousand attributes raises a challenge to traditional clustering mechanisms. However, our experimental result shows that apps that have similar behaviors (due to having been developed from the same company or providing similar services) can be clustered together via both SOMs and GHSOMs. © 2013 IEEE.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings of the International Joint Conference on Neural Networks,2013, 論文編號 6706728, Dallas, TX; United States; 4 August 2013 到 9 August 2013; 類別編號CFP13IJS-ART; 代碼 102436
dc.relation (關聯) 10.1109/IJCNN.2013.6706728
dc.subject (關鍵詞) App stores; Data preprocessing; Implicit systems; Large dimensions; Mobile apps; Static binary analysis; System methods; Traditional clustering; Neural networks; Conformal mapping
dc.title (題名) Clustering iOS executable using self-organizing maps
dc.type (資料類型) conferenceen