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博碩士論文 etd-0522114-000113 詳細資訊
Title page for etd-0522114-000113
論文名稱
Title
以混合式分析方法為基礎之行動惡意軟體快速偵測
Fast Mobile Malware Detection Based on Hybrid Analysis Method
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
48
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2014-07-14
繳交日期
Date of Submission
2016-03-21
關鍵字
Keywords
靜態分析、基因演算法、惡意軟體、安卓、逆向工程
Android, malware, genetic algorithm, static analysis, reverse engineering
統計
Statistics
本論文已被瀏覽 5936 次,被下載 32
The thesis/dissertation has been browsed 5936 times, has been downloaded 32 times.
中文摘要
行動裝置普及率越來越高,惡意軟體的數量也隨之增加,如何保障行動裝置用戶的裝置安全成為重要的議題。
目前主流的行動裝置惡意軟體偵測方式主要分為動態跟靜態兩種方式,動態分析方法透過行動裝置實際的行為來判斷應用程式是否為惡意,但如何處發惡意行為以及偵測效率是動態分析的一大難處,而靜態分析則多無法確認惡意軟體的惡意行為為何。
Android 是行動裝置作業系統的主流,也是惡意軟體主要針對的目標,Android應用程式是利用Java開發,因此較容易進行逆向工程。本研究對逆向工程後的原始碼進行資料流程分析,並從中擷取特徵值,再透過基因演算法(Genetic algorithm)找出能辨識出惡意行為的特徵用以偵測惡意軟體,在1,259個惡意軟體及Google Play下載的1,259個善意軟體中,本研究的實驗中可以偵測出96.5%的惡意軟體並且有90%的精確度。
Abstract
More and more people nowadays use mobile devices. Mobile malwares are also increase very quickly. How to protect mobile devices become an important issue.
The two main kinds of approaches to detect mobile malwares are static approaches and dynamic approaches. Dynamic approaches detect malware base on the actual behaviors of applications but how to trigger malicious behavior and the efficient of dynamic approaches are the difficulties of this kind of approaches. Most of the static approaches cannot know what malicious behaviors malwares will conduct.
Android is the most popular mobile platform and the main target of malwares. Because Android applications are developed using Java programing language, it’s easier to get application source codes using reverse engineering techniques. The proposed system using data flow analysis on source codes reverse from applications to extract feature. Then using genetic algorithm to obtain features which are helpful to distinguish malicious behaviors. We conduct an experiment on 1,259 malwares and 1,259 benign applications downloaded from Google Play. We can detect 96.5% of the malwares and have precision with 90%.
目次 Table of Contents
[I. INTRODUCTION 1]
[1.1 Purpose 3]
[II. RELATED WORK 5]
[2.1 Comparison to mainstream mobile malware analysis approach 5]
[2.1.1 Analysis based on permission 5]
[2.1.2 Dynamic analysis 6]
[2.1.3 Static analysis 7]
[2.2 Introducing to Android platform 9]
[2.2.1 Android APK file 9]
[2.2.2 Reverse engineering tools 9]
[2.3 Feature selection 10]
[2.3.1 Mutual information 10]
[2.4 Genetic algorithm 10]
[III. Proposed System 11]
[3.1 System architecture 11]
[3.1.1 De-compilation component 12]
[3.1.2 Threat pattern building component 12]
[IV. System Evaluation 32]
[4.1 Malware samples 32]
[4.2 Evaluation 34]
[V. Conclusion and System limitations 40]
[References 41]
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