التفاصيل البيبلوغرافية
| العنوان: |
Electromagnetic Side-Channel Analysis Methods for Digital Forensics on Internet of Things |
| المؤلفون: |
Sayakkara, Asanka P. |
| المساهمون: |
orcid:0000-0001-9558-7913 |
| بيانات النشر: |
University College Dublin. School of Computer Science |
| سنة النشر: |
2022 |
| المجموعة: |
University College Dublin: Research Repository UCD |
| مصطلحات موضوعية: |
Digital forensics, Internet of Things, Electromagnetic side-channel analysis, Machine learning |
| الوصف: |
Modern legal and corporate investigations heavily rely on the field of digital forensics to uncover vital evidence. The dawn of the Internet of Things (IoT) devices has expanded this horizon by providing new kinds of evidence sources that were not available in traditional digital forensics. However, unlike desktop and laptop computers, the bespoke hardware and software employed on most IoT devices obstructs the use of classical digital forensic evidence acquisition methods. This situation demands alternative approaches to forensically inspect IoT devices. Electromagnetic Side-Channel Analysis (EM-SCA) is a branch in information security that exploits Electromagnetic (EM) radiation of computers to eavesdrop and exfiltrate sensitive information. A multitude of EM-SCA methods have been demonstrated to be effective in attacking computing systems under various circumstances. The objective of this thesis is to explore the potential of leveraging EM-SCA as a forensic evidence acquisition method for IoT devices. Towards this objective, this thesis formulates a model for IoT forensics that uses EM-SCA methods. The design of the proposed model enables the investigators to perform complex forensic insight gathering procedures without having expertise in the field of EM-SCA. In order to demonstrate the function of the proposed model, a proof-of-concept was implemented as an open-source software framework called EMvidence. This framework utilises a modular architecture following a Unix philosophy; where each module is kept minimalist and focused on extracting a specific forensic insight from a specific IoT device. By doing so, the burden of dealing with the diversity of the IoT ecosystem is distributed from a central point into individual modules. Under the proposed model, this thesis presents the design, the implementation, and the evaluation of a collection of methods that can be used to acquire forensic insights from IoT devices using their EM radiation patterns. These forensic insights include detecting ... |
| نوع الوثيقة: |
doctoral or postdoctoral thesis |
| اللغة: |
English |
| Relation: |
http://hdl.handle.net/10197/12821 |
| الاتاحة: |
http://hdl.handle.net/10197/12821 |
| Rights: |
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/ |
| رقم الانضمام: |
edsbas.7FAF75C9 |
| قاعدة البيانات: |
BASE |