| dc.contributor | 資訊管理學系 | |
| dc.creator (作者) | 郁方;陳郁方 | zh_TW |
| dc.date (日期) | 2013 | |
| dc.date.accessioned | 20-Apr-2016 17:15:34 (UTC+8) | - |
| dc.date.available | 20-Apr-2016 17:15:34 (UTC+8) | - |
| dc.date.issued (上傳時間) | 20-Apr-2016 17:15:34 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/85825 | - |
| dc.description.abstract (摘要) | 當行動應用程式充斥生活之中, 對其真實行為進行正規與系統性的分析變得格外重要。 本計劃提出以靜態二元分析技術對行動應用程式碼進行行為分析與特徵化。 運用分析 工具與反轉工程技術，重建行動應用程式的原碼特徵。蒐集與分析惡意程式的行為特 徵，針對外部/使用者開發的行動應用程式，偵測其可能包含的惡意行為，從而預防濳 藏的隱私洩露與後門攻擊。技術面運用雲端架構達成分散式字元分析，運用自動機與 模型檢驗技術達成因果分析，運用統計與人工智慧進行應用程式分類與特徵萃取。 本計劃亦將實現於一自動化分析行動程式工具。 | |
| dc.description.abstract (摘要) | In this project, we propose to investigate static binary analysis techniques on iOS executables with the aim of providing an automatic, sound and formal approach to analyze and characterize mobile applications, and detect (potential) malicious behaviors that are embedded and may not appear to users. The project can be divided into three parts: distributed syntax analysis based on reverse engineering techniques and the hadoop framework, causality analysis based on string analysis, automata and model checking techniques, and malware classification and feature extraction based on statistic and artificial intelligence approaches. A new tool AppBeach (abbrv. on App Behavior Architect) that provides an end to end static binary analysis on iOS executables is also presented. | |
| dc.format.extent | 4442814 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | 計畫編號 NSC 102-2221-E004-002 | |
| dc.title (題名) | 行動應用程式的靜態二元分析 | zh_TW |
| dc.title.alternative (其他題名) | Static Binary Analysis on iOS Executables | |
| dc.type (資料類型) | report | |
