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

Wine, Fraud and Expertise

While fraud has existed in various forms throughout the history of wine, the establishment of the fine and rare wine market generated increased opportunities and incentives for producing counterfeit wine. In the contemporary fine and rare wine market, wine fraud is a serious concern. The past several decades witnessed significant events of fine wine forgery, including the infamous Jefferson bottles and the more recent large-scale counterfeit operation orchestrated by Rudy Kurniawan. These events prompted and renewed market interest in wine authentication and fraud detection. Expertise in wine is characterized by the relationship between subjective and objective judgments. The development of the wine fraud expert draws attention to the emergence of expertise as an industry response to wine fraud and the relationship between expert judgment and modern science

DNA barcoding as a molecular tool to track down mislabeling and food piracy

DNA barcoding is a molecular technology that allows the identification of any biological species by amplifying, sequencing and querying the information from genic and/or intergenic standardized target regions belonging to the extranuclear genomes. Although these sequences represent a small fraction of the total DNA of a cell, both chloroplast and mitochondrial barcodes chosen for identifying plant and animal species, respectively, have shown sufficient nucleotide diversity to assess the taxonomic identity of the vast majority of organisms used in agriculture. Consequently, cpDNA and mtDNA barcoding protocols are being used more and more in the food industry and food supply chains for food labeling, not only to support food safety but also to uncover food piracy in freshly commercialized and technologically processed products. Since the extranuclear genomes are present in many copies within each cell, this technology is being more easily exploited to recover information even in degraded samples or transformed materials deriving from crop varieties and livestock species. The strong standardization that characterizes protocols used worldwide for DNA barcoding makes this technology particularly suitable for routine analyses required by agencies to safeguard food safety and quality. Here we conduct a critical review of the potentials of DNA barcoding for food labeling along with the main findings in the area of food piracy, with particular reference to agrifood and livestock foodstuffs

Police overestimation of criminal career homogeneity

Police presumptions about criminal career trajectories have been little studied. The exploratory study reported here involved 42 police staff of varying rank and experience. Participants were asked to complete a questionnaire which asked them to predict the type of offence that an individual with specified prior record was most likely to commit next. Participating police personnel substantially overstated the homogeneity of criminal careers, i.e. the nature of prior offences determined their prediction of their next offence more than available official data would deem reasonable. An incidental finding was that officers who rated the probability of further offending highest were also those who thought criminal careers most specialised. Theimplications for operational police decision making are discussed and held to be profound