The Malware Analysis Body of Knowledge (MABOK)

The ability to forensically analyse malicious software (malware) is becoming an increasingly important discipline in the field of Digital Forensics. This is because malware is becoming stealthier, targeted, profit driven, managed by criminal organizations, harder to detect and much harder to analyse. Malware analysis requires a considerable skill set to delve deep into malware internals when it is designed specifically to detect and hinder such attempts. This paper presents a foundation for a Malware Analysis Body of Knowledge (MABOK) that is required to successfully forensically analyse malware. This body of knowledge has been the result of several years of research into malware dissection

Cloud Forensics Investigations Relationship: A Model And Instrument

Cloud computing is one of the most important advances in computing in recent history. cybercrime has developed side by side and rapidly in recent years. Previous studies had confirmed the existing gap between cloud service providers (CSPs) and law enforcement agencies (LEAs), and LEAs cannot work without the cooperation of CSPs. Their relationship is influenced by legal, organisational and technical dimensions, which affect the investigations. Therefore, it is essential to enhance the cloud forensics relationship between LEAs and CSPs. This research addresses the need for a unified collaborative model to facilitate proper investigations and explore and evaluate existing different models involved in the relationship between Omani LEAs and local CSPs as a participant in investigations. Further, it proposes a validated research instrument that can be cloud forensics survey. It can also be used as an evaluation tool to identify, measure, and manage cloud forensic investigations

Digital Forensics Investigation Frameworks for Cloud Computing and Internet of Things

Rapid growth in Cloud computing and Internet of Things (IoT) introduces new vulnerabilities that can be exploited to mount cyber-attacks. Digital forensics investigation is commonly used to find the culprit and help expose the vulnerabilities. Traditional digital forensics tools and methods are unsuitable for use in these technologies. Therefore, new digital forensics investigation frameworks and methodologies are required. This research develops frameworks and methods for digital forensics investigations in cloud and IoT platforms

A Revised Forensic Process for Aligning the Investigation Process with the Design of Forensic-Enabled Cloud Services

© Springer Nature Switzerland AG 2020. The design and implementation of cloud services, without taking under consideration the forensic requirements and the investigation process, makes the acquisition and examination of data, complex and demanding. The evidence gathered from the cloud may not become acceptable and admissible in the court. A literature gap in supporting software engineers so as to elicit and model forensic-related requirements exists. In order to fill the gap, software engineers should develop cloud services in a forensically sound manner. In this paper, a brief description of the cloud forensic-enabled framework is presented (adding some new elements) so as to understand the role of the design of forensic-enabled cloud services in a cloud forensic investigation. A validation of the forensic requirements is also produced by aligning the stages of cloud forensic investigation process with the framework’s forensic requirements. In this way, on one hand, a strong relationship is built between these two elements and emphasis is given to the role of the forensic requirements and their necessity in supporting the investigation process. On the other hand, the alignment assists towards the identification of the degree of the forensic readiness of a cloud service against a forensic investigation

Towards Increasing Trust In Expert Evidence Derived From Malware Forensic Tools

Following a series of high profile miscarriages of justice in the UK linked to questionable expert evidence, the post of the Forensic Science Regulator was created in 2008. The main objective of this role is to improve the standard of practitioner competences and forensic procedures. One of the key strategies deployed to achieve this is the push to incorporate a greater level of scientific conduct in the various fields of forensic practice. Currently there is no statutory requirement for practitioners to become accredited to continue working with the Criminal Justice System of England and Wales. However, the Forensic Science Regulator is lobbying the UK Government to make this mandatory. This paper focuses upon the challenge of incorporating a scientific methodology to digital forensic investigations where malicious software (‘malware’) has been identified. One aspect of such a methodology is the approach followed to both select and evaluate the tools used to perform dynamic malware analysis during an investigation. Based on the literature, legal, regulatory and practical needs we derive a set of requirements to address this challenge. We present a framework, called the ‘Malware Analysis Tool Evaluation Framework’ (MATEF), to address this lack of methodology to evaluate software tools used to perform dynamic malware analysis during investigations involving malware and discuss how it meets the derived requirements

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats

Towards an Automated Digital Data Forensic Model with specific reference to Investigation Processes

Existing digital forensics frameworks do not provide clear guidelines for conducting digital forensics investigation. However, had a framework existed, investigations based on known procedures and processes would follow strict prescribed standardisation. This should direct investigations following a set method for comparisons; ensuring future investigation is following one standard. Digital forensics lack confirmed and tested methods; this became obvious when we consider varied interpretations of the same case by participants using different investigation methods. Previous research covered several approaches to setting a forensics framework, which are mere adaptations of previous models. We found that only a few models present a framework that defines or delivers qualified likeness between the different disciplines. From this, possible pattern analysis from different disciplines is possible (Kohn, 2007). This underlines the need to standardise processes, to ensure proven and consistent results. Digital Forensics Science needs a new approach, defining and standardising investigation processes by affirming an investigation framework. Present research does not enough cover how existing forensic frameworks are used as guideline while conduct investigations. As a result, wide general interpretations are possible instead of following a set standard. Investigation processes and in particular how data confirmation is conducted during and after investigation becomes questionable as well. This also challenges data consistency and the legality of investigation processes when a non-standard framework is used without forming a sound theory based on proven models