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Degree: PhD  Dates: Last 2 Years

You searched for subject:(Digital forensics). Showing records 1 – 2 of 2 total matches.

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University of Illinois – Urbana-Champaign

1. Palmer, Imani Nkechinyere. Forensic analysis of computer evidence.

Degree: PhD, Computer Science, 2018, University of Illinois – Urbana-Champaign

Digital forensics is the science involved in the discovery, preservation, and analysis of evidence on digital devices. The end goal of digital forensics is to determine the events that occurred, who performed them, and how were they performed. In order for an investigation to lead to a sound conclusion, it must demonstrate that it is the product of sound scientific methodology. Digital forensics is inundated with many problems. These problems include an insufficient number of capable examiners, without a standard for certification there is a lack of training for examiners and current tools are unable to deal with the more complex cases, and lack of intelligent automation. This work perpetuates the ability of computer science principles to digital forensics creates a basis of acceptance for digital forensics in both the legal and forensic science community. This work focuses on three solutions. In terms of education, there is a lack of mandatory standardization, certification, and accreditation. Currently, there is a lack of standards in the interpretation of forensic evidence. The current techniques used by forensic investigators during analysis generally involve ad-hoc methods based on the vague and untested understanding of the system. These forensic techniques are the root of the significant differences in the testimony conducted by digital forensic expert witnesses. Lastly, digital forensic expert witness testimony is under great scrutiny because of the lack of standards in both education and investigative methods. To remedy this situation, we developed multiple avenues to facilitate more effective investigations. To improve the availability and standardization of education, we developed a multidisciplinary digital forensics curriculum. To improve the standards of forensic evidence interpretation, we developed a methodology based on graph theory to develop a logical view of low-level forensic data. To improve the admissibility of evidence, we developed a methodology to assign a likelihood to the hypotheses determined by forensic investigators. Together, these methods significantly improve the effectiveness of digital forensic investigations. Overall, this work calls the computer science community to join forces with the digital forensics community in order to develop, test and implement established computer science methodology in the application of digital forensics. Advisors/Committee Members: Campbell, Roy H (advisor), Campbell, Roy H (Committee Chair), Bates, Adam (committee member), Gunter, Carl (committee member), Kesan, Jay (committee member), Gelfand, Boris (committee member).

Subjects/Keywords: Digital Forensics; Graph Theory; Digital Forensic Investigations

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APA (6th Edition):

Palmer, I. N. (2018). Forensic analysis of computer evidence. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101030

Chicago Manual of Style (16th Edition):

Palmer, Imani Nkechinyere. “Forensic analysis of computer evidence.” 2018. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 15, 2019. http://hdl.handle.net/2142/101030.

MLA Handbook (7th Edition):

Palmer, Imani Nkechinyere. “Forensic analysis of computer evidence.” 2018. Web. 15 Dec 2019.

Vancouver:

Palmer IN. Forensic analysis of computer evidence. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2142/101030.

Council of Science Editors:

Palmer IN. Forensic analysis of computer evidence. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101030


University of Pretoria

2. Pieterse, Heloise. Evaluation and Identification of Authentic Smartphone Data.

Degree: PhD, Computer Science, 2019, University of Pretoria

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… Advisors/Committee Members: Olivier, Martin S. (advisor), Van Heerden, Renier (coadvisor).

Subjects/Keywords: UCTD; Digital Forensics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pieterse, H. (2019). Evaluation and Identification of Authentic Smartphone Data. (Doctoral Dissertation). University of Pretoria. Retrieved from http://hdl.handle.net/2263/70669

Chicago Manual of Style (16th Edition):

Pieterse, Heloise. “Evaluation and Identification of Authentic Smartphone Data.” 2019. Doctoral Dissertation, University of Pretoria. Accessed December 15, 2019. http://hdl.handle.net/2263/70669.

MLA Handbook (7th Edition):

Pieterse, Heloise. “Evaluation and Identification of Authentic Smartphone Data.” 2019. Web. 15 Dec 2019.

Vancouver:

Pieterse H. Evaluation and Identification of Authentic Smartphone Data. [Internet] [Doctoral dissertation]. University of Pretoria; 2019. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2263/70669.

Council of Science Editors:

Pieterse H. Evaluation and Identification of Authentic Smartphone Data. [Doctoral Dissertation]. University of Pretoria; 2019. Available from: http://hdl.handle.net/2263/70669

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