Digital currency forensics

The banknote manufacturing industry is shrouded in secrecy, the fundamental mechanics of security components are closely guarded trade secrets. Currency forensics is the application of systematic methods to determine authenticity of questioned currency. However, forensic analysis is a difficult task requiring specially trained examiners, the most important challenge is systematically and methodologically repeating the analysis process to reduce human error and time. In this thesis, an empirical approach for automated currency forensics is formulated and a prototype is developed. A two-part feature vector is defined comprised of colour features and texture features. Finally the note in question is classified by a Feedforward Neural Network (FNN) and a measurement of similarity between template and suspect note is output. Colorspace performance have been compared namely the: RGB, HSI, and Lab colorspaces. It is found that the combined average between the RGB channels known as the Intensity channel provides the highest discriminability, and is selected as the candidate colorspace. By its definition the word currency refers to an agreed medium for exchange, a nation’s currency is the formal medium enforced by the elected governing entity. Forensic science is the application of scientific methods to answer questions of a legal nature. Throughout history, issuers have faced one common threat, the threat of counterfeit. Despite technological advancements, overcoming counterfeit production remains a distant reality. Scientific determination of authenticity requires a deep understanding of the raw materials, and manufacturing processes involved. This thesis serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desired, a robust currency is one which withstands security breaches, and is durable surpassing the lifetime of the current currency. It has been identified that the current currency forensic investigation process is a manual ad-hoc process requiring specialist sought after questioned document examiners (QDEs), clearly this process is subject to human error. In a forensic setting, the analysis process must be systematic, methodological and repeatable. The digital currency forensics system addresses the issue of currency analysis by implementing a specific repeatable process through an automated examination using a combination of image processing, and classification techniques. This is achieved by implementing machine learning and pattern recognition

A Method of Mining Key Accounts from Internet Pyramid Selling Data

Internet pyramid selling causes great harm and has difficulty in obtaining evidence. As most of internet pyramid selling often uses virtual coins to complete cash settlement, criminals can exchange virtual coins by controlling visual accounts to transfer funds illegally. The purpose of the work is to mine the key accounts and build evidence chain of personnel and funds. In order to deal with a large number of transaction data, we characterize the virtual currency trading data, which is divided into seven transaction characteristics. The key accounts are these outliers which have too prominent trading behaviour. So we use positive samples to train One Class Support Vector Machine (OCSVM) classification to find trading behaviours’ boundary and detect outliers in classification. Then, the correlation between member’s information and pyramid selling hierarchical relationship can also be obtained by further linking the trading behaviour of key accounts. Finally, we can screen out the virtual accounts directly controlled by the pyramid selling organization, analyse the flow of funds, investigate and obtain evidence on amount of money involved in pyramid selling. The experimental results show that the classification model cannot only establish the normal model for account transfer behaviour, but also effectively identify abnormal transfer behaviour, thus improving efficiency of investigation and evidence collection for economic investigation departments

An empirical approach for currency identification

Currency identification is the application of systematic methods to determine authenticity of questioned currency. However, identification analysis is a difficult task requiring specially trained examiners, the most important challenge is automating the analysis process reducing human labor and time. In this study, an empirical approach for automated currency identification is formulated and a prototype is developed. A two parts feature vector is defined comprised of color features and texture features. Finally the banknote in question is classified by a Feedforward Neural Network (FNN) and a measurement of the similarity between existing samples and suspect banknote is output

Innovative public governance through cloud computing: Information privacy, business models and performance measurement challenges

Purpose: The purpose of this paper is to identify and analyze challenges and to discuss proposed solutions for innovative public governance through cloud computing. Innovative technologies, such as federation of services and cloud computing, can greatly contribute to the provision of e-government services, through scaleable and flexible systems. Furthermore, they can facilitate in reducing costs and overcoming public information segmentation. Nonetheless, when public agencies use these technologies, they encounter several associated organizational and technical changes, as well as significant challenges. Design/methodology/approach: We followed a multidisciplinary perspective (social, behavioral, business and technical) and conducted a conceptual analysis for analyzing the associated challenges. We conducted focus group interviews in two countries for evaluating the performance models that resulted from the conceptual analysis. Findings: This study identifies and analyzes several challenges that may emerge while adopting innovative technologies for public governance and e-government services. Furthermore, it presents suggested solutions deriving from the experience of designing a related platform for public governance, including issues of privacy requirements, proposed business models and key performance indicators for public services on cloud computing. Research limitations/implications: The challenges and solutions discussed are based on the experience gained by designing one platform. However, we rely on issues and challenges collected from four countries. Practical implications: The identification of challenges for innovative design of e-government services through a central portal in Europe and using service federation is expected to inform practitioners in different roles about significant changes across multiple levels that are implied and may accelerate the challenges' resolution. Originality/value: This is the first study that discusses from multiple perspectives and through empirical investigation the challenges to realize public governance through innovative technologies. The results emerge from an actual portal that will function at a European level. © Emerald Group Publishing Limited

Safeguarding the Evidential Value of Forensic Cryptocurrency Investigations

Analyzing cryptocurrency payment flows has become a key forensic method in law enforcement and is nowadays used to investigate a wide spectrum of criminal activities. However, despite its widespread adoption, the evidential value of obtained findings in court is still largely unclear. In this paper, we focus on the key ingredients of modern cryptocurrency analytics techniques, which are clustering heuristics and attribution tags. We identify internationally accepted standards and rules for substantiating suspicions and providing evidence in court and project them onto current cryptocurrency forensics practices. By providing an empirical analysis of CoinJoin transactions, we illustrate possible sources of misinterpretation in algorithmic clustering heuristics. Eventually, we derive a set of legal key requirements and translate them into a technical data sharing framework that fosters compliance with existing legal and technical standards in the realm of cryptocurrency forensics. Integrating the proposed framework in modern cryptocurrency analytics tools could allow more efficient and effective investigations, while safeguarding the evidential value of the analysis and the fundamental rights of affected persons

A Simple Experiment with Microsoft Office 2010 and Windows 7 Utilizing Digital Forensic Methodology

Digital forensic examiners are tasked with retrieving data from digital storage devices, and frequently these examiners are expected to explain the circumstances that led to the data being in its current state. Through written reports or verbal, expert testimony delivered in court, digital forensic examiners are expected to describe whether data have been altered, and if so, then to what extent have data been altered. Addressing these expectations results from opinions digital forensic examiners reach concerning their understanding of electronic storage and retrieval methods. The credibility of these opinions evolves from the scientific basis from which they are drawn using forensic methodology. Digital forensic methodology, being a scientific process, is derived from observations and repeatable findings in controlled environments. Furthermore, scientific research methods have established that causal conclusions can be drawn only when observed in controlled experiments. With this in mind, it seems beneficial that digital forensic examiners have a library of experiments from which they can perform, observe results, and derive conclusions. After having conducted an experiment on a specific topic, a digital forensic examiner will be in a better position to express with confidence the state of the current data and perhaps the conditions that led to its current state. This study provides a simple experiment using the contemporary versions of the most widely used software applications running on the most commonly installed operation system. Here, using the Microsoft Office 2010 applications, a simple Word document, an Excel spreadsheet, a PowerPoint presentation, and an Access database are created and then modified. A forensic analysis is performed to determine the extent in which the changes to the data are identified. The value in this study is not that it yields new forensic analysis techniques, but rather that it illustrates a methodology that other digital forensic examiners can apply to develop experiments representing their specific data challenges

Beyond a Fistful of Tumblers: Toward a Taxonomy of Ethereum-based Mixers

The role played by decentralised services in the obfuscation of crypto-asset transactions performed on transparent blockchains has increasingly captured the attention of regulators. This is exemplified by the headlines about the U.S. Treasury\u27s sanctions on the Ethereum-based mixer Tornado Cash. Yet, despite the existing controversies on the use of mixers, the different functionalities of these information systems with an inherent dark side remain to be explored by the literature. So far, contributions primarily encompass technical works and studies that focus on the Bitcoin ecosystem. This paper puts forward a multi-layer taxonomy of the smart-contract-based - and, therefore, functionally richer - family of mixers on Ethereum. Our proposed taxonomy is grounded on (1) a review of existing literature, (2) an analysis of mixers\u27 project documentation, (3) their corresponding smart contracts, and (4) expert interviews. The evaluation included the application of the taxonomy to two mixers - RAILGUN and zkBob. Our taxonomy represents a valuable tool for law enforcement, regulators, and other stakeholders to explore critical properties affecting compliance and use of Ethereum-based mixers