A user-oriented network forensic analyser: the design of a high-level protocol analyser

Network forensics is becoming an increasingly important tool in the investigation of cyber and computer-assisted crimes. Unfortunately, whilst much effort has been undertaken in developing computer forensic file system analysers (e.g. Encase and FTK), such focus has not been given to Network Forensic Analysis Tools (NFATs). The single biggest barrier to effective NFATs is the handling of large volumes of low-level traffic and being able to exact and interpret forensic artefacts and their context – for example, being able extract and render application-level objects (such as emails, web pages and documents) from the low-level TCP/IP traffic but also understand how these applications/artefacts are being used. Whilst some studies and tools are beginning to achieve object extraction, results to date are limited to basic objects. No research has focused upon analysing network traffic to understand the nature of its use – not simply looking at the fact a person requested a webpage, but how long they spend on the application and what interactions did they have with whilst using the service (e.g. posting an image, or engaging in an instant message chat). This additional layer of information can provide an investigator with a far more rich and complete understanding of a suspect’s activities. To this end, this paper presents an investigation into the ability to derive high-level application usage characteristics from low-level network traffic meta-data. The paper presents a three application scenarios – web surfing, communications and social networking and demonstrates it is possible to derive the user interactions (e.g. page loading, chatting and file sharing ) within these systems. The paper continues to present a framework that builds upon this capability to provide a robust, flexible and user-friendly NFAT that provides access to a greater range of forensic information in a far easier format

An Automated Approach for Digital Forensic Analysis of Heterogeneous Big Data

The major challenges with big data examination and analysis are volume, complex interdependence across content, and heterogeneity. The examination and analysis phases are considered essential to a digital forensics process. However, traditional techniques for the forensic investigation use one or more forensic tools to examine and analyse each resource. In addition, when multiple resources are included in one case, there is an inability to cross-correlate findings which often leads to inefficiencies in processing and identifying evidence. Furthermore, most current forensics tools cannot cope with large volumes of data. This paper develops a novel framework for digital forensic analysis of heterogeneous big data. The framework mainly focuses upon the investigations of three core issues: data volume, heterogeneous data and the investigators cognitive load in understanding the relationships between artefacts. The proposed approach focuses upon the use of metadata to solve the data volume problem, semantic web ontologies to solve the heterogeneous data sources and artificial intelligence models to support the automated identification and correlation of artefacts to reduce the burden placed upon the investigator to understand the nature and relationship of the artefacts

Cyber security investigation for Raspberry Pi devices

Big Data on Cloud application is growing rapidly. When the cloud is attacked, the investigation relies on digital forensics evidence. This paper proposed the data collection via Raspberry Pi devices, in a healthcare situation. The significance of this work is that could be expanded into a digital device array that takes big data security issues into account. There are many potential impacts in health area. The field of Digital Forensics Science has been tagged as a reactive science by some who believe research and study in the field often arise as a result of the need to respond to event which brought about the needs for investigation; this work was carried as a proactive research that will add knowledge to the field of Digital Forensic Science. The Raspberry Pi is a cost-effective, pocket sized computer that has gained global recognition since its development in 2008; with the wide spread usage of the device for different computing purposes. Raspberry Pi can potentially be a cyber security device, which can relate with forensics investigation in the near future. This work has used a systematic approach to study the structure and operation of the device and has established security issues that the widespread usage of the device can pose, such as health or smart city. Furthermore, its evidential information applied in security will be useful in the event that the device becomes a subject of digital forensic investigation in the foreseeable future. In healthcare system, PII (personal identifiable information) is a very important issue. When Raspberry Pi plays a processor role, its security is vital; consequently, digital forensics investigation on the Raspberry Pies becomes necessary

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

Auditing database systems through forensic analysis

The majority of sensitive and personal data is stored in a number of different Database Management Systems (DBMS). For example, Oracle is frequently used to store corporate data, MySQL serves as the back-end storage for many webstores, and SQLite stores personal data such as SMS messages or browser bookmarks. Consequently, the pervasive use of DBMSes has led to an increase in the rate at which they are exploited in cybercrimes. After a cybercrime occurs, investigators need forensic tools and methods to recreate a timeline of events and determine the extent of the security breach. When a breach involves a compromised system, these tools must make few assumptions about the system (e.g., corrupt storage, poorly configured logging, data tampering). Since DBMSes manage storage independent of the operating system, they require their own set of forensic tools. This dissertation presents 1) our database-agnostic forensic methods to examine DBMS contents from any evidence source (e.g., disk images or RAM snapshots) without using a live system and 2) applications of our forensic analysis methods to secure data. The foundation of this analysis is page carving, our novel database forensic method that we implemented as the tool DBCarver. We demonstrate that DBCarver is capable of reconstructing DBMS contents, including metadata and deleted data, from various types of digital evidence. Since DBMS storage is managed independently of the operating system, DBCarver can be used for new methods to securely delete data (i.e., data sanitization). In the event of suspected log tampering or direct modification to DBMS storage, DBCarver can be used to verify log integrity and discover storage inconsistencies

The Forensic Curator: Digital Forensics as a Solution to Addressing the Curatorial Challenges Posed by Personal Digital Archives

The growth of computing technology during the previous three decades has resulted in a large amount of content being created in digital form. As their creators retire or pass away, an increasing number of personal data collections, in the form of digital media and complete computer systems, are being offered to the academic institutional archive. For the digital curator or archivist, the handling and processing of such digital material represents a considerable challenge, requiring development of new processes and procedures. This paper outlines how digital forensic methods, developed by the law enforcement and legal community, may be applied by academic digital archives. It goes on to describe the strategic and practical decisions that should be made to introduce forensic methods within an existing curatorial infrastructure and how different techniques, such as forensic hashing, timeline analysis and data carving, may be used to collect information of a greater breadth and scope than may be gathered through manual activities

Assessing the evidential value of artefacts recovered from the cloud

Cloud computing offers users low-cost access to computing resources that are scalable and flexible. However, it is not without its challenges, especially in relation to security. Cloud resources can be leveraged for criminal activities and the architecture of the ecosystem makes digital investigation difficult in terms of evidence identification, acquisition and examination. However, these same resources can be leveraged for the purposes of digital forensics, providing facilities for evidence acquisition, analysis and storage. Alternatively, existing forensic capabilities can be used in the Cloud as a step towards achieving forensic readiness. Tools can be added to the Cloud which can recover artefacts of evidential value. This research investigates whether artefacts that have been recovered from the Xen Cloud Platform (XCP) using existing tools have evidential value. To determine this, it is broken into three distinct areas: adding existing tools to a Cloud ecosystem, recovering artefacts from that system using those tools and then determining the evidential value of the recovered artefacts. From these experiments, three key steps for adding existing tools to the Cloud were determined: the identification of the specific Cloud technology being used, identification of existing tools and the building of a testbed. Stemming from this, three key components of artefact recovery are identified: the user, the audit log and the Virtual Machine (VM), along with two methodologies for artefact recovery in XCP. In terms of evidential value, this research proposes a set of criteria for the evaluation of digital evidence, stating that it should be authentic, accurate, reliable and complete. In conclusion, this research demonstrates the use of these criteria in the context of digital investigations in the Cloud and how each is met. This research shows that it is possible to recover artefacts of evidential value from XCP