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https://nccur.lib.nccu.edu.tw/handle/140.119/69198
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| Title: | 行動應用軟體在迭代分群行為之研究
Iterative Clustering on Behaviors of App Executables |
| Authors: | 邱莉晴
Chiu, Li Ching |
| Contributors: | 郁方
Yu, Fang
邱莉晴
Chiu, Li Ching |
| Keywords: | 行動應用程式
GHSOM
分群
App
Clustering
GHSOM
iterative |
| Date: | 2013 |
| Issue Date: | 2014-08-25 15:16:54 (UTC+8) |
| Abstract: | 行動裝置在現在這個世代相當普遍，而我們需要一個方法來探索App在背後的行為。
本研究提出了一個非監督式的分群方式，目的是在於探討我們是否能使用App中的原始碼當作以行為分群的依據。
在此研究中，我們應用了迭代分群的方式對Apps做分析，並且觀察分群的結果是否恰當。
而在實驗中，我們由App Store下載了數百個App並加以分析，我們發現我們所提出的方式表現相當良好並且能給出正確的分群結果。
Smart devices are everywhere nowadays. Mobile application (app) development has become one of the main streams in software industry with more than millions of apps that have been developed and published to billions of users.
It is essential to have a systematic way to analyze apps, preferably on their executable that are the only public available sources of apps in most cases.
In this work, we propose to apply unsupervised clustering to mobile applications on their system call distributions. This is done by first adopting a static binary analysis that reverses engineering on executable of apps to find method call/sequence counts that are embedded in apps. Apps are then clustered iteratively based on this information to reveal implicit relationships among apps based on function call similarity. The GHSOM (Growing Hierarchical Self-Organizing Map), an unsupervised learning tool, is integrated to cluster apps based on the information resolved from their executable directly.
We use types of methods and sequences as features. To run the clustering algorithm on apps, however, we immediately confront a problem that we have a large amount of attributes and data that leads to a long/infeasible analysis time with GHSOMs. The new iterative approach is proposed to conquer this problem along with dimension reduction with principle component analysis, cutting attributes with limited information loss.
In the preliminary result on analyzing hundreds of apps that are directly downloaded from Apple app store, we can find that the proposed clustering works well and reveals some interesting information. Apps that are developed by the same company are clustered in the same group. Apps that have similar behaviors, e.g., having the same functions on games, painting, socializing, are clustered together. |
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| Description: | 碩士
國立政治大學
資訊管理研究所
101356040
102 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G1013560401 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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Files in This Item:
| File |
Size | Format | |
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| 040101.pdf | 1971Kb | Adobe PDF2 | 115 | View/Open |
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