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

Business intelligence as the support of decision-making processes in e-commerce systems environment

The present state of world economy urges managers to look for new methods, which can help to start the economic growth. To achieve this goal, managers use standard as well as new procedures. The fundamental prerequisite of the efficient decision-making processes are actual and right information. Managers need to monitor past information and current actual information to generate trends of future development based on it. Managers always should define strictly what do they want to know, how do they want to see it and for what purpose do they want to use it. Only in this case they can get right information applicable to efficient decision-making. Generally, managers´ decisions should lead to make the customers´ decision-making process easier. More frequently than ever, companies use e-commerce systems for the support of their business activities. In connection with the present state and future development, cross-border online shopping growth can be expected. To support this, companies will need much better systems providing the managers adequate and sufficient information. This type of information, which is usually multidimensional, can be provided by the Business Intelligence (BI) technologies. Besides special BI systems, some of BI technologies are obtained in quite a few of ERP (Enterprise Resource Planning) systems. One of the crucial questions is whether should companies and firms buy or develop special BI software, or whether they can use BI tools contained in some ERP systems. In respect of this, there is a question if the modern ERP systems can provide the managers sufficient possibilities relating to ad-hoc reporting, static and dynamic reports and OLAP analyses. A one of the main goals of this article is to show and verify Business Intelligence tools of Microsoft Dynamics NAV for the support of decision-making in terms of the cross-border online purchasing. Pursuant to above-mentioned, in this article authors deal with problems relating to managers´ decision-making, customers´ decision-making and a support of its using the BI tools contained in ERP system Microsoft Dynamics NAV. A great deal of this article is aimed at area of multidimensional data which are the source data of e-commerce systems.Business Intelligence, decision-making, e-commerce system, cross-border online purchasing, multi-dimensional data, reporting, data visualization

An efficient closed frequent itemset miner for the MOA stream mining system

Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version