English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 94188/124659 (76%)
Visitors : 29676924      Online Users : 508
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 >  Item 140.119/74638
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/74638

    Title: Clustering iOS executable using self-organizing maps
    Authors: Yu, F.;Huang, S.-Y.;Chiou, L.-C.;Tsaih, R.-H.
    Contributors: 資管系
    Keywords: App stores;Data preprocessing;Implicit systems;Large dimensions;Mobile apps;Static binary analysis;System methods;Traditional clustering;Neural networks;Conformal mapping
    Date: 2013
    Issue Date: 2015-04-16 17:36:38 (UTC+8)
    Abstract: 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.
    Relation: 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
    Data Type: conference
    Appears in Collections:[資訊管理學系] 會議論文

    Files in This Item:

    File Description SizeFormat

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback