Data mining for detecting Bitcoin Ponzi schemes

Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives

Systemic acquired critique of credit card deception exposure through machine learning

Artigo publicado em revista científica internacionalA wide range of recent studies are focusing on current issues of financial fraud, especially concerning cybercrimes. The reason behind this is even with improved security, a great amount of money loss occurs every year due to credit card fraud. In recent days, ATM fraud has decreased, while credit card fraud has increased. This study examines articles from five foremost databases. The literature review is designed using extraction by database, keywords, year, articles, authors, and performance measures based on data used in previous research, future research directions and purpose of the article. This study identifies the crucial gaps which ultimately allow research opportunities in this fraud detection process by utilizing knowledge from the machine learning domain. Our findings prove that this research area has become most dominant in the last ten years. We accessed both supervised and unsupervised machine learning techniques to detect cybercrime and management techniques which provide evidence for the effectiveness of machine learning techniques to control cybercrime in the credit card industry. Results indicated that there is room for further research to obtain better results than existing ones on the basis of both quantitative and qualitative research analysis.info:eu-repo/semantics/publishedVersio

Implementing Peer Group Analysis within a Track and Trace System to Detect Potential Fraud(s)

Tracking and tracing of goods movement is a key requirement for supply chain management and analysis. Data collection can be broad and large in volumes. Goods can moves in complex supply chain distributions, where disputes, frauds and thefts can happens. This paper aimed to develop a practical method to analyze the incoming data and employ unsupervised potential fraud detection in near real-time. The method is designed and discussed around peer group analysis (PGA) approach which is commonly used in financial market. The paper shall focus on two steps. First, monitor and groups good movements and categorize vendors or suppliers with similar trend / behaviours into dedicatedpeers. Second build a tool / services that detect anomalies in event transactions. The monitoring serviceshalldetect the outlier orindividual objects that distinct from peers whichpotentially fraud /alerts

Using hidden Markov models in credit card transaction fraud detection

In this paper we shall propose a credit card transaction fraud detection framework which uses Hidden Markov Models, a well established technology that has not as yet been tested in this area and through which we aim to address the limitations posed by currently used technologies. Hidden Markov Models have for many years been effectively implemented in other similar areas. The flexibility offered by these models together with the similarity in concepts between Automatic Speech Recognition and credit card fraud detection has instigated the idea of testing the usefulness of these models in our area of research. The study performed in this project investigated the utilisation of Hidden Markov Models by means of proposing a number of different frameworks, which frameworks are supported through the use of clustering and profiling mechanisms. The profiling mechanisms are used in order to build Hidden Markov Models which are more specialised and thus are deployed on training data that is specific to a set of cardholders which have similar spending behaviours. Clustering techniques were used in order to establish the association between different classes of transactions. Two different clustering algorithms were tested in order to determine the most effective one. Also, different Hidden Markov Models were built using different criteria for test data. The positive results achieved portray the effectiveness of these models in classifying fraudulent and legitimate transactions through a resultant percentage value which indicates the prominence of the transaction being contained in the respective model.peer-reviewe

Unethical consumers: Deshopping behaviour using the qualitative analysis of theory of planned behaviour and accompanied (de)shopping

Purpose Previous research indicates that deshopping is a prevalent and growing consumer behaviour. This paper examines deshopping from a consumer perspective, and applies the Theory of Planned Behaviour (TPB) to demonstrate how this behaviour can be managed and prevented. An accompanied (de)shop is also conducted. This paper also places deshopping within a legal and ethical context, in relation to the established literature in this field. Methodology approach This paper tests the TPB variables in a qualitative way by conducting in-depth interviews with deshoppers, who had completed a quantitative questionnaire. The results further support and enhance the quantitative TPB results collected previously with 535 consumers. An accompanied (de)shop is also reviewed, as this qualitative research technique, enables an enhanced understanding and evidence of the deshopping process, which has not been demonstrated previously. The findings demonstrate support for these qualitative research tool, which enable a deeper understanding of the deshopping process and its management. Findings The findings demonstrate important use of the TPB as a qualitative research technique. The model is also expanded and redesigned by adding additional variables as a result of this research. The accompanied (de)shop findings demonstrate support for this qualitative research tool, which also enables a deeper understanding of the deshopping process and its management. Practical implications The research concludes with the implications of deshopping for the industry and makes recommendations as how to reduce deshopping, as well as recommending the qualitative research techniques utilised to future researchers. Originality This paper has identified the key variables that influence deshopping, and demonstrates that procedures can be designed to reduce this behaviour by manipulating the TPB variables. This paper has also added additional variables to the TPB model, which have proved to be influential in deshopping behaviour, thereby developing theoretical knowledge of TPB. The use of the TPB has also provided a theoretical underpinning to utilising a consumer education program to prevent problem behaviours. This research demonstrates that this could alter deshoppers’ attitudes and subjective norms. This is also the first paper to place deshopping in a legal framework which highlights the legal loopholes in a retailer’s returns policy and the implications of new directives which will influence retailer’s abilities to refuse a return. This paper is also the first to explore deshopping within an ethical framework that has created new knowledge on the unethical consumer in relation to deshopping behaviour. This study also incorporates an accompanied (de)shop methodology; this form of research has never been undertaken in relation to deshopping activity and has generated completely new knowledge of what is happening when the actual behaviour is taking place

Cyber Safety: A theoretical Insight

This paper is written by the EUCPN Secretariat following the topic of the Estonian Presidency of the Network, which is Cyber Safety. It gives a theoretical insight in what Cyber Safety is. Furthermore, we take interest in what the exact object is of cybercrime and have a deeper look into two European policy priorities, namely cyber-attacks and payment fraud. Moreover, these priorities are the subject of the European Crime Prevention award. The goal of this paper is to add to the digital awareness of local policy-makers and practitioners on a theoretical level. A toolbox will follow with legislative measures, existing policies and best practices on this topic

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review