简析手机取证中的数据提取

近些年来,不法分子利用智能手机实施犯罪时有发生。为有效打击这类犯罪行为,手机取证已成为必不可少的技术。通过手机取证,公安机关从手机中提取数据,然后经分析后形成证据,提取手机数据是手机取证的重要环节。对手机取证进行简要分析,以Android手机为例探讨数据的存储架构和提取方法。     

新型智能终端取证技术研究

随着移动互联网的广泛应用,智能手机、平板等新型智能终端设备在各种各样的违法犯罪活动中开始扮演越来越重要的角色,从涉案手机中提取的数据常常包含与违法犯罪行为相关的重要线索和证据。然而,移动智能终端设备不断提升的安全设计可能使得取证人员无法从设备中提取数据,给电子数据取证鉴定工作提出了新的挑战。本文详细分析当前主流的iOS、Android和Windows Phone等平台下的移动设备的安全机制,研究了主要的安全机制破解和取证技术及其在目前电子数据取证工作中的应用。最后,对未来面向新型移动智能终端电子数据取证技术研究发展方向进行了探讨。     

针对智能手机应用程序的取证技术研究

针对智能手机的研究是近几年相关领域的研究热点。作为一种新型设备,智能手机的取证技术是取证人员面临的新问题。随着用户数量的暴涨,与手机相关的突发事件和犯罪案件逐年上升。深入研究智能手机的取证方法和取证技术已经迫在眉睫。主要针对智能手机存储卡的文件组织形式,有重点的介绍几种常见手机应用的取证方法。     

基于Android平台的手机取证技术

随着网络信息技术的普及,利用手机进行犯罪的案件呈上升趋势。智能手机取证成为司法行政机关用于侦破此类案件的关键。手机取证的信息来源多为手机机身内存、SIM卡,部分信息可由相关部门授权从移动网络运营商处获取。获取手机自身证据信息的常用方式有root技术、USB调试模式、JTAG提取技术及Chipoff提取方法。取证人员可根据具体检材选择合适的证据提取方式。     

手机规范取证研究

着立法滞后,规范等问题,取证的方法．在司法实践中,涉及到手机的案件越来越多,手机取证的重要性日益突出,但在取证中却存在移动通信技术检验机构不完善,缺少高素质的取证人员,技术落后,缺乏取证的意识和流程不阻碍了手机电子证据在司法实践中的应用。文章针对手机取证中存在的问题,探讨了规范手机为手机取证的规范化提供理论参考。     

Android手机取证技术研究综述

Android智能手机的广泛应用使其成为备受关注的数字取证源,近年来取证技术不断发展,Android手机取证标准日臻成熟.研究了Android手机存储设备NAND闪存的异步更新策略这一有利于取证的特性,详细描述了符合国际标准的Android手机取证过程,以及Android手机取证技术的国内外研究现状和面临的挑战.在此基础上展望未来发展趋势,为进一步研究Android手机取证提供参考性意见.     

智能手机取证研究

智能手机的普及使对手机取证技术的研究迈向新的高度。该文主要对智能手机中证据表现形式、取证方法及注意事项进行分析,介绍了一些取证工具,最后提出了此领域中存在的一些问题及解决方法。     

智能手机数据的提取与恢复研究

随着移动通信技术的发展,智能手机逐渐成为人们工作生活中必备的通讯工具,与此同时手机犯罪也与日俱增,手机数据的提取与恢复是打击这类犯罪的有效手段,手机取证技术的研究迈向新的高度.主要介绍了手机数据存储分类、数据获取的方法以及数据的提取与恢复.     

一种改进的静态取证数据挖掘算法

随着计算机犯罪案件的日益增加,采集犯罪证据的计算机取证技术已成为目前的研究热点。计算机取证有事后的静态取证和实时的动态取证两种方法。静态取证的关键是从海量的数据中筛选挖掘出与案件相关的、反映案件客观事实的、有效的犯罪证据信息。基于已有静态取证分析方法的不足,文章提出了一种改进的静态取证数据挖掘算法,并通过对大量数据的测试证明,该算法不但可行而且准确性及效率较高。     

公安政法院校开设电子数据课程探讨

基于教学实践,在对电子数据取证专业人才需求的研究基础上,探讨如何在网络安全与执法本科专业和网络保卫执法技术方向警务硕士教学中设置电子数据相关课程,包括课程设置和实验设计等内容,使相关专业的毕业生能够较为系统地掌握电子数据的基本理论、技术和方法,以胜任公安和法庭等对专业化计算机犯罪的侦破与电子数据取证工作。     

基于手机的取证调查模型研究

给出了手机取证的概念,并与计算机取证进行了比较,分析了手机取证和计算机取证的差异。结合手机取证的特点和难点,提出了基于手机的取证调查模型,分析了模型中各个阶段的具体活动。该模型对取证人员具有一定的指导意义。     

Android移动终端取证综述

随着移动智能终端的普及,利用移动终端进行欺骗,敲诈的犯罪刑事案件正在呈现逐年上涨的趋势,对移动终端的深入取证工作迫在眉睫。为了开展后续研究,先对Android移动终端的取证研究现状,取证工具,取证技术进行概述,分析对比国内外的研究成果,提出下一步研究计划。     

Instagram Mobile Application Digital Forensics

In this research, we developed a plugin for our automated digital forensics framework to extract and preserve the evidence from the Android and the IOS-based mobile phone application, Instagram. This plugin extracts personal details from Instagram users, e.g., name, user name, mobile number, ID, direct text or audio, video, and picture messages exchanged between different Instagram users. While developing the plugin, we identified resources available in both Android and IOS-based devices holding key forensics artifacts. We highlighted the poor privacy scheme employed by Instagram. This work, has shown how the sensitive data posted in the Instagram mobile application can easily be reconstructed, and how the traces, as well as the URL links of visual messages, can be used to access the privacy of any Instagram user without any critical credential verification. We also employed the anti-forensics method on the Instagram Android’s application and were able to restore the application from the altered or corrupted database file, which any criminal mind can use to set up or trap someone else. The outcome of this research is a plugin for our digital forensics ready framework software which could be used by law enforcement and regulatory agencies to reconstruct the digital evidence available in the Instagram mobile application directories on both Android and IOS-based mobile phones.     

Android系统智能手机语音应用开发环境构架

3G无线网络技术的飞速发展促进了移动终端硬件性能不断提升.具有多功能、多应用和服务“智能化”等特点的新一代智能手机正在逐渐取代传统的功能手机.着眼智能手机的语音应用开发,选取Android操作系统智能手机,介绍其语音应用开发环境的构架,并提出基于此构架的语音应用开发解决方案.     

手机仿真取证系统浅析——Android系统仿真实战

随着各种手机的推广,手机应用程序的极大丰富,在实际取证工作中,我们经常会遇到对各种手机的应用程序取证,而现有对手机应用程序的分析技术刚刚起步,要实现对大部分应用程序使用痕迹的取证需要一段较长的时间。如何快速对手机应用程序进行取证成为取证工作的一个重要。因此本文提出了一个手机仿真取证系统的解决方案,并以Android系统为例进行介绍。     

Potential cyberterrorism via a multimedia smart phone based on a web 2.0 application via ubiquitous Wi-Fi access points and the corresponding digital forensics

Cyberterrorism has become a hotly debated research issue in the past decades because of the convergence of mobile computing powers and the fledging multimedia communication computing capabilities. Cyberterrorism is the exploitation of computer network tools to incur malfunction or shut down critical infrastructures with several keyboard punches, which is dramatically different from traditional terrorism. Due to the ubiquitous multimedia communication tools, they have radically transformed the ways concerning data transmission. Unfortunately, it also incurs unprecedented opportunities for committing cyber crimes that we were not able to foresee two decades ago. Undoubtedly, the mushrooming proliferation of mobile phones spectacularly triggers the information security leakage while most people heavily rely on mobile phones for daily communication. As cybercrime or cyberterrorism surges, digital forensics (DF) of mobile communication devices still enormously lags behind than computer forensics. Hence, in this research paper, we provide a hypothetical case review concerning the DF of a potential cyberterrorist attack that was triggered by a mobile multimedia smart phone utilizing a popular web 2.0 application program via ubiquitous Wi-Fi access points. The corresponding DF of the mobile device was conducted in a step-by-step manner as well as the crime scene reconstruction based on the digital evidence collected, analyzed, and preserved.     

Integrated Approach to Detect Cyberbullying Text: Mobile Device Forensics Data

Mobile devices and social networks provide communication opportunities among the young generation, which increases vulnerability and cybercrimes activities. A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters. This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit. We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection. We use forensics techniques, Machine Learning (ML), and Deep Learning (DL) algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the case. Experiments on a real-time dataset reveal better results for the detection of cyberbullying content. The Random Forest in ML approach produces 87% of accuracy without SMOTE technique, whereas the value of F1Score produces a good result with SMOTE technique. The LSTM has 92% of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.