国产bbaaaaa片,成年美女黄网站色视频免费,成年黄大片,а天堂中文最新一区二区三区,成人精品视频一区二区三区尤物

首頁(yè)> 外文期刊>Digital investigation >Automated forensic analysis of mobile applications on Android devices
【24h】

Automated forensic analysis of mobile applications on Android devices

機(jī)譯：對(duì)Android設(shè)備上的移動(dòng)應(yīng)用程序進(jìn)行自動(dòng)取證分析

獲取原文
獲取原文并翻譯 | 示例
  • 摘要
  • 著錄項(xiàng)
  • 相似文獻(xiàn)
  • 相關(guān)主題

摘要

It is not uncommon that mobile phones are involved in criminal activities, e.g., the surreptitious collection of credit card information. Forensic analysis of mobile applications plays a crucial part in order to gather evidences against criminals. However, traditional forensic approaches, which are based on manual investigation, are not scalable to the large number of mobile applications. On the other hand, dynamic analysis is hard to automate due to the burden of setting up the proper runtime environment to accommodate OS differences and dependent libraries and activate all feasible program paths. We propose a fully automated tool, Fordroid for the forensic analysis of mobile applications on Android. Fordroid conducts inter-component static analysis on Android APKs and builds control flow and data dependency graphs. Furthermore, Fordroid identifies what and where information written in local storage with taint analysis. Data is located by traversing the graphs. This addresses several technique challenges, which include inter-component string propagation, string operations (e.g., append) and API invocations. Also, Fordroid identifies how the information is stored by parsing SQL commands, i.e., the structure of database tables. Finally, we selected 100 random Android applications consisting of 2841 components from four categories for evaluation. Analysis of all apps took 64 h. Fordroid discovered 469 paths in 36 applications that wrote sensitive information (e.g., GPS) to local storage. Furthermore, Fordroid successfully located where the information was written for 458 (98%) paths and identified the structure of all (22) database tables. (C) 2018 The Author(s). Published by Elsevier Ltd on behalf of DFRWS.
機(jī)譯：手機(jī)參與犯罪活動(dòng)（例如秘密收集信用卡信息）并不罕見(jiàn)。為了收集針對(duì)犯罪分子的證據(jù)，對(duì)移動(dòng)應(yīng)用程序進(jìn)行取證分析至關(guān)重要。但是，基于手動(dòng)調(diào)查的傳統(tǒng)取證方法無(wú)法擴(kuò)展到大量移動(dòng)應(yīng)用程序。另一方面，由于設(shè)置適當(dāng)?shù)倪\(yùn)行時(shí)環(huán)境以適應(yīng)OS差異和相關(guān)庫(kù)以及激活所有可行程序路徑的負(fù)擔(dān)，動(dòng)態(tài)分析很難自動(dòng)化。我們提出了一種全自動(dòng)工具Fordroid，用于對(duì)Android上的移動(dòng)應(yīng)用程序進(jìn)行取證分析。 Fordroid在Android APK上進(jìn)行組件間靜態(tài)分析，并構(gòu)建控制流和數(shù)據(jù)依賴圖。此外，F(xiàn)ordroid通過(guò)污點(diǎn)分析識(shí)別在本地存儲(chǔ)中寫(xiě)入的信息和位置。通過(guò)遍歷圖形來(lái)定位數(shù)據(jù)。這解決了一些技術(shù)挑戰(zhàn)，包括組件間字符串傳播，字符串操作（例如，append）和API調(diào)用。而且，F(xiàn)ordroid通過(guò)解析SQL命令（即數(shù)據(jù)庫(kù)表的結(jié)構(gòu)）來(lái)標(biāo)識(shí)信息的存儲(chǔ)方式。最后，我們從四個(gè)類別中選擇了100個(gè)包含2841個(gè)組件的隨機(jī)Android應(yīng)用程序進(jìn)行評(píng)估。所有應(yīng)用程序的分析花費(fèi)了64小時(shí)。 Fordroid在36個(gè)將敏感信息（例如GPS）寫(xiě)入本地存儲(chǔ)的應(yīng)用程序中發(fā)現(xiàn)了469條路徑。此外，F(xiàn)ordroid成功地找到了用于458（98％）條路徑的信息寫(xiě)入位置，并確定了所有（22）數(shù)據(jù)庫(kù)表的結(jié)構(gòu)。 （C）2018作者。由Elsevier Ltd代表DFRWS發(fā)布。

著錄項(xiàng)

相似文獻(xiàn)

  • 外文文獻(xiàn)
  • 中文文獻(xiàn)
  • 專利
獲取原文

客服郵箱：kefu@zhangqiaokeyan.com

京公網(wǎng)安備：11010802029741號(hào) ICP備案號(hào)：京ICP備15016152號(hào)-6 六維聯(lián)合信息科技 (北京) 有限公司?版權(quán)所有

  • 客服微信

  • 服務(wù)號(hào)