Circumventing iOS security mechanisms for APT forensic investigations: A security taxonomy for cloud apps

被引:27
作者
D'Orazio, Christian J. [1 ]
Choo, Kim-Kwang Raymond [1 ,2 ]
机构
[1] Univ South Australia, Sch Informat Technol & Math Sci, Adelaide, SA, Australia
[2] Univ Texas San Antonio, Dept Informat Syst & Cyber Secur, San Antonio, TX 78249 USA
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2018年 / 79卷
关键词
Advanced persistent threat investigations; iOS cloud apps; iOS cloud forensics; iOS security taxonomy;
D O I
10.1016/j.future.2016.11.010
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Mobile devices and apps such as cloud apps are a potential attack vector in an advanced persistent threat (APT) incident, due to their capability to store sensitive data (e.g. backup of private and personal data in digital repositories) and access sensitive resources (e.g. compromising the device to access an organisational network). These devices and apps are, thus, a rich source of digital evidence. It is vital to be able to identify artefacts of forensic interest transmitted to/from and stored on the devices. However, security mechanisms in mobile platforms and apps can complicate the forensic acquisition of data. In this paper, we present techniques to circumvent security mechanisms and facilitate collection of artefacts from cloud apps. We then demonstrate the utility of the circumvention techniques using 18 popular iOS cloud apps as case studies. Based on the findings, we present the first iOS cloud app security taxonomy that could be used in the investigation of an APT incident. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:247 / 261
页数:15
相关论文
共 40 条
[11]   A critical review of 7 years of Mobile Device Forensics [J].
Barmpatsalou, Konstantia ;
Damopoulos, Dimitrios ;
Kambourakis, Georgios ;
Katos, Vasilios .
DIGITAL INVESTIGATION, 2013, 10 (04) :323-349
[12]  
Bellare M, 2000, LECT NOTES COMPUT SC, V1807, P139
[13]  
Bellare M., 1994, CRYPTO, P232
[14]  
Byrnes J., 2016, APPADVICE
[15]   A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code [J].
Cen, Lei ;
Gates, Christoher S. ;
Si, Luo ;
Li, Ninghui .
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2015, 12 (04) :400-412
[16]  
CHOO KKR, 2006, P 19 IEEE COMP SEC F, P297, DOI DOI 10.1109/CSFW.2006.26
[17]  
Clark W., 2014, BETTER
[18]  
Cornea O., 2016, IOS APPL SECURITY TE
[19]  
D'Orazio C. J., 2016, FUTURE GENE IN PRESS
[20]   An adversary model to evaluate DRM protection of video contents on iOS devices [J].
D'Orazio, Christian ;
Choo, Kim-Kwang Raymond .
COMPUTERS & SECURITY, 2016, 56 :94-110