Evaluation and Identification of Authentic Smartphone Data

Abstract

Mobile technology continues to evolve in the 21st century, providing end-users with mobile devices that support improved capabilities and advance functionality. This ever-improving technology allows smartphone platforms, such as Google Android and Apple iOS, to become prominent and popular among end-users. The reliance on and ubiquitous use of smartphones render these devices rich sources of digital data. This data becomes increasingly important when smartphones form part of regulatory matters, security incidents, criminal or civil cases. Digital data is, however, susceptible to change and can be altered intentionally or accidentally by end-users or installed applications. It becomes, therefore, essential to evaluate the authenticity of data residing on smartphones before submitting the data as potential digital evidence.

This thesis focuses on digital data found on smartphones that have been created by smartphone applications and the techniques that can be used to evaluate and identify authentic data. Identification of authentic smartphone data necessitates a better understanding of the smartphone, the related smartphone applications and the environment in which the smartphone operates. Derived from the conducted research and gathered knowledge are the requirements for authentic smartphone data. These requirements are captured in the smartphone data evaluation model to assist digital forensic professionals with the assessment of smartphone data. The smartphone data evaluation model, however, only stipulates how to evaluate the smartphone data and not what the outcome of the evaluation is. Therefore, a classification model is constructed using the identified requirements and the smartphone data evaluation model. The classification model presents a formal classification of the evaluated smartphone data, which is an ordered pair of values. The first value represents the grade of the authenticity of the data and the second value describes the completeness of the evaluation. Collectively, these models form the basis for the developed SADAC tool, a proof of concept digital forensic tool that assists with the evaluation and classification of smartphone data.

To conclude, the evaluation and classification models are assessed to determine the effectiveness and efficiency of the models to evaluate and identify authentic smartphone data. The assessment involved two attack scenarios to manipulate smartphone data and the subsequent evaluation of the effects of these attack scenarios using the SADAC tool. The results produced by evaluating the smartphone data associated with each attack scenario confirmed the classification of the authenticity of smartphone data is feasible. Digital forensic professionals can use the provided models and developed SADAC tool to evaluate and identify authentic smartphone data.

The outcome of this thesis provides a scientific and strategic approach for evaluating and identifying authentic smartphone data, offering needed assistance to digital forensic professionals. This research also adds to the field of digital forensics by providing insights into smartphone forensics, architectural components of smartphone applications and the nature of authentic smartphone data.