IPCFA: A Methodology for Acquiring Forensically-Sound Digital Evidence in the Realm of IAAS Public Cloud Deployments

Cybercrimes and digital security breaches are on the rise: savvy businesses and organizations of all sizes must ready themselves for the worst. Cloud computing has become the new normal, opening even more doors for cybercriminals to commit crimes that are not easily traceable. The fast pace of technology adoption exceeds the speed by which the cybersecurity community and law enforcement agencies (LEAs) can invent countermeasures to investigate and prosecute such criminals. While presenting defensible digital evidence in courts of law is already complex, it gets more complicated if the crime is tied to public cloud computing, where storage, network, and computing resources are shared and dispersed over multiple geographical areas. Investigating such crimes involves collecting evidence data from the public cloud that is court-sound. Digital evidence court admissibility in the U.S. is governed predominantly by the Federal Rules of Evidence and Federal Rules of Civil Procedures. Evidence authenticity can be challenged by the Daubert test, which evaluates the forensic process that took place to generate the presented evidence. Existing digital forensics models, methodologies, and processes have not adequately addressed crimes that take place in the public cloud. It was only in late 2020 that the Scientific Working Group on Digital Evidence (SWGDE) published a document that shed light on best practices for collecting evidence from cloud providers. Yet SWGDE’s publication does not address the gap between the technology and the legal system when it comes to evidence admissibility. The document is high level with more focus on law enforcement processes such as issuing a subpoena and preservation orders to the cloud provider. This research proposes IaaS Public Cloud Forensic Acquisition (IPCFA), a methodology to acquire forensic-sound evidence from public cloud IaaS deployments. IPCFA focuses on bridging the gap between the legal and technical sides of evidence authenticity to help produce admissible evidence that can withstand scrutiny in U.S. courts. Grounded in design research science (DSR), the research is rigorously evaluated using two hypothetical scenarios for crimes that take place in the public cloud. The first scenario takes place in AWS and is hypothetically walked-thru. The second scenario is a demonstration of IPCFA’s applicability and effectiveness on Azure Cloud. Both cases are evaluated using a rubric built from the federal and civil digital evidence requirements and the international best practices for iv digital evidence to show the effectiveness of IPCFA in generating cloud evidence sound enough to be considered admissible in court

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

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

Big Data Techniques to Improve Learning Access and Citizen Engagement for Adults in Urban Environments

This presentation explores the emerging concept of ‘Big Data in Education’ and introduces novel technologies and approaches for addressing inequalities in access to participation and success in lifelong learning, to produce better life outcomes for urban citizens. It introduces the work of the new Urban Big Data Centre (UBDC) at the University of Glasgow, presenting a case study of its first data product – the integrated Multimedia City Data (iMCD) project. Educational engagement and predictive factors are presented for adult learners, and older adult learners, in a representative survey of 1500 households. This was followed up with mobility tracking data using GPS data and wearable camera images, as well as one year’s worth of contextual data from over one hundred web sources (social media, news, weather). The chapter introduces the complex dataset that can help stakeholders, academics, citizens and other external users examine active aging and citizen learning engagement in the modern urban city, and thus support the development of the learning city. It concludes with a call for a more three-dimensional view of citizen-learners’ daily activity and mobility, such as satellite, mobile phone and active travel application data, alongside administrative data linkage to further explore lifelong learning participation and success. Policy implications are provided for addressing inequalities, and interventions proposed for how cities might promote equal and inclusive adult learning engagement in the face of continued austerity cuts and falling adult learner numbers

Forecasting in the light of Big Data

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on the first principles, and the naive inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

Reversing the Question: Does Happiness Affect Consumption and Savings Behavior?

I examine the impact of happiness on consumption and savings behavior using data from the DNB Household Survey from the Netherlands and the German Socio-Economic Panel. Instrumenting individual happiness with regional sunshine, the results suggest that happier people save more, spend less, and have a lower marginal propensity to consume. Happier people take more time for making decisions and have more control over expenditures; they expect a longer life and (accordingly) seem more concerned about the future than the present; they also expect less inflation in the future.happiness, savings, consumption, weather

Proceedings of the 15th Australian Digital Forensics Conference, 5-6 December 2017, Edith Cowan University, Perth, Australia

Conference Foreword This is the sixth year that the Australian Digital Forensics Conference has been held under the banner of the Security Research Institute, which is in part due to the success of the security conference program at ECU. As with previous years, the conference continues to see a quality papers with a number from local and international authors. 8 papers were submitted and following a double blind peer review process, 5 were accepted for final presentation and publication. Conferences such as these are simply not possible without willing volunteers who follow through with the commitment they have initially made, and I would like to take this opportunity to thank the conference committee for their tireless efforts in this regard. These efforts have included but not been limited to the reviewing and editing of the conference papers, and helping with the planning, organisation and execution of the conference. Particular thanks go to those international reviewers who took the time to review papers for the conference, irrespective of the fact that they are unable to attend this year. To our sponsors and supporters a vote of thanks for both the financial and moral support provided to the conference. Finally, to the student volunteers and staff of the ECU Security Research Institute, your efforts as always are appreciated and invaluable. Yours sincerely, Conference ChairProfessor Craig ValliDirector, Security Research Institute Congress Organising Committee Congress Chair: Professor Craig Valli Committee Members: Professor Gary Kessler – Embry Riddle University, Florida, USA Professor Glenn Dardick – Embry Riddle University, Florida, USA Professor Ali Babar – University of Adelaide, Australia Dr Jason Smith – CERT Australia, Australia Associate Professor Mike Johnstone – Edith Cowan University, Australia Professor Joseph A. Cannataci – University of Malta, Malta Professor Nathan Clarke – University of Plymouth, Plymouth UK Professor Steven Furnell – University of Plymouth, Plymouth UK Professor Bill Hutchinson – Edith Cowan University, Perth, Australia Professor Andrew Jones – Khalifa University, Abu Dhabi, UAE Professor Iain Sutherland – Glamorgan University, Wales, UK Professor Matthew Warren – Deakin University, Melbourne Australia Congress Coordinator: Ms Emma Burk