An Android Communication App Forensic Taxonomy

Azfar, Abdullah; Choo, Kim‐Kwang Raymond; Liu, Lin

doi:10.1111/1556-4029.13164

Cited by 46 publications

(29 citation statements)

References 28 publications

(26 reference statements)

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“…The main body contains the developed model. For instance, Xian’s model “Symbian smartphones forensic process model” [31] covered a five processes for Symbian smartphones. We extracted the related concepts under each of these processes.…”

Section: Mobile Forensic Metamodelmentioning

confidence: 99%

A metamodel for mobile forensics investigation domain

et al. 2017

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With the rapid development of technology, mobile phones have become an essential tool in terms of crime fighting and criminal investigation. However, many mobile forensics investigators face difficulties with the investigation process in their domain. These difficulties are due to the heavy reliance of the forensics field on knowledge which, although a valuable resource, is scattered and widely dispersed. The wide dispersion of mobile forensics knowledge not only makes investigation difficult for new investigators, resulting in substantial waste of time, but also leads to ambiguity in the concepts and terminologies of the mobile forensics domain. This paper developed an approach for mobile forensics domain based on metamodeling. The developed approach contributes to identify common concepts of mobile forensics through a development of the Mobile Forensics Metamodel (MFM). In addion, it contributes to simplifying the investigation process and enables investigation teams to capture and reuse specialized forensic knowledge, thereby supporting the training and knowledge management activities. Furthermore, it reduces the difficulty and ambiguity in the mobile forensics domain. A validation process was performed to ensure the completeness and correctness of the MFM. The validation was conducted using two techniques for improvements and adjustments to the metamodel. The last version of the adjusted metamodel was named MFM 1.2.

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Section: Mobile Forensic Metamodelmentioning

confidence: 99%

A metamodel for mobile forensics investigation domain

et al. 2017

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“…Azfar et al [3] extend their previous work [2,4,5] to 30 popular Android productivity apps. Based on the findings of the forensic examination of these apps, a two-dimensional taxonomy of the forensic artefacts of the productivity apps is presented.…”

Section: Forensicsmentioning

confidence: 93%

Guest Editorial: Multimedia Social Network Security and Applications

Zhang

Choo

2016

Multimed Tools Appl

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In the increasingly inter-connected society, multimedia social networks (MSN) have become a 'mainstream' tool used by online users to connect and share contents with other users 24/7 in real-time. It is, therefore, unsurprising that MSNs have become a salient area of inquiry by computer scientists and computer security researchers. For example, researchers need to design intelligent computing and soft computing technologies to improve multimedia system functions, efficiency and performance, as well as improving user's sharing experiences (e.g. using recommendation systems and more effective algorithms). Ensuring the security of users and data are also an ongoing topic of interest and importance due to the ease in producing and sharing user and multimedia content using MSNs. In recent years, we have seen advances in multimedia system security and soft computing for MSN applications, such as Bhard^security mechanisms (e.g. protocols and methodologies) and Bsoft^computing methods (e.g. machine learning and rough set), as well as research efforts in trust assessment, risk management and social factors to understand the trade-off between the effectiveness and security in MSNs.This special issue is dedicated to the reporting of state-of-the-art and recent advancements in this emerging area of enquiry. We received 26 submissions for this special issue, of which 12 were accepted for publication (i.e. 46 % acceptance rate). Each paper went through a rigorous peer review process, in addition to multiple follow-up rounds with the authors. A summary of the papers is categorized and presented below.

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“…[81][82][83][84] In the context of cloud forensics, mobile devices can provide large amounts of relevant information such as copies of downloaded files, unencrypted credential information, sync logs, databases of file synchronization caches, and user configuration files. [81][82][83][84] In the context of cloud forensics, mobile devices can provide large amounts of relevant information such as copies of downloaded files, unencrypted credential information, sync logs, databases of file synchronization caches, and user configuration files.…”

Section: Analysis Of Cloudme Mobile Clientsmentioning

confidence: 99%

“…The pervasive use of smartphone devices caused a demand for mobile forensics. [81][82][83][84] In the context of cloud forensics, mobile devices can provide large amounts of relevant information such as copies of downloaded files, unencrypted credential information, sync logs, databases of file synchronization caches, and user configuration files. 27,28,85 Our examinations of the CloudMe mobile clients determined that the data directory is located…”

Section: Analysis Of Cloudme Mobile Clientsmentioning

confidence: 99%

CloudMe forensics: A case of big data forensic investigation

Teing

Dehghantanha

Choo

2017

Concurrency and Computation

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The significant increase in the volume, variety, and velocity of data complicates cloud forensic efforts, and such (big) evidential data will, at some point, become too (computationally) expensive to be fully identified, collected, and analysed in a timely manner. Thus, it is important for digital forensic practitioners to have an up-to-date knowledge of relevant data artefacts that could be forensically recovered from the cloud product under investigation. In this paper, CloudMe, a popular cloud storage service, is studied. The types and locations of the artefacts relating to the installation and uninstallation of CloudMe client application, logging in and out, and file synchronization events from the computer desktop and mobile clients are described. Findings from this research will also help inform future development of tools and techniques (e.g., data mining techniques) for cloud-enabled big data endpoint forensics investigation.

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An Android Communication App Forensic Taxonomy

Cited by 46 publications

References 28 publications

A metamodel for mobile forensics investigation domain

A metamodel for mobile forensics investigation domain

Guest Editorial: Multimedia Social Network Security and Applications

CloudMe forensics: A case of big data forensic investigation

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