Digital forensic process model for mobile business devices: smart technologies
Worldwide usage of mobile SMART devices has been dramatically increased over the past decade. The popularity of these devices has also grown as a result of the increase in terms of their processing power, large storage capacity and large memory. Mobile SMART devices such as SMART phones, tablet, phaplets and Personal Digital Assistants (PDAs) are now very common and very much part of most businesses’ network. As a result, these devices hold enormous amounts of both personal and private business data. Consequently, they have become the target for criminals. They have been found to be involved in criminal activities particularly cybercrimes. These devices are often seized as part of a criminal investigation, and this has led to the need to acquire the data contained in these devices. The SMART device data has become potential evidence in criminal cases.
The vital information held by these mobile SMART devices trigger the need for mobile SMART device forensic capability. The primary aim of digital forensic is to identify the digital information and capture all potential evidence in the device, including call logs, phone book data, text messages, and so on. This process is very important therefore, potential evidence must not be altered in the process so it can be admissible in a court of law. This requires following standardised investigation procedures. However, there is currently no standardised digital forensic investigation process model for SMART devices.
Yet, there are a number of digital investigation process models available. However, they were either developed for a specific sub-field such as computer forensics, mobile forensics, and network forensics or, a generic digital forensic investigation model. This study is aimed to fill the gap identified in the literature; there is no investigation process model that can be used on an investigation that involves multi-disciplinary requirements. The question raised here is “What can be done to improve the effectiveness and efficiency of digital forensic investigation for SMART devices?” this question will be answered in chapter seven.
This study involves developing of a new digital forensic investigation process model and a framework. To answer the research question and make sure that the new artefact is evaluated and refined to a high standard, the Design Science (DS) research method is employed to guide the study. DS method defines the processes from identifying and defining of the problem to communicating the findings through scholarly and professional publications. The DS method influences the design of the study and the evaluation methodology employed to evaluate the artefact which is done in the fifth phase of the DS research method. As a result, this study found that the problem and the gap identified in the literature are real because digital forensics has a complex nature and it needs multi-disciplinary capabilities and abilities. The implication is that for a SMART device – that brings convergence of many segregated areas - investigation knowledge from each of the implicated areas will be required for effective and efficient investigations. As a result, this study found that employing the current investigation process models in an investigation in a multi-disciplinary environment, the effectiveness and efficiency of the investigation is compromised. The completed study contributes to the body of knowledge in the field of digital forensics when a multi-disciplinary investigation process model is required that will help an investigator in an environment where more than one sub-field of digital forensics is present. The experimental test data was analysed and the results were used to improve the multi-disciplinary model and develop a multi-disciplinary investigation framework that can be continuously improved as technology and devices change. The professional significance is for improvement in the effectiveness and efficiency of digital forensic investigation processes in a multi-disciplinary environment.