In our highly digitalized society, cybercrime has become a common crime. However, because research into cybercriminals is in its infancy, our knowledge about cybercriminals is still limited. One of the main considerations is whether cybercriminals have higher intellectual capabilities than traditional criminals or even the general population. Although criminological studies clearly show that traditional criminals have lower intellectual capabilities, little is known about the relationship between cybercrime and intelligence. The current study adds to the literature by exploring the relationship between CITO-test scores and cybercrime in the Netherlands. The CITO final test is a standardized test for primary school students - usually taken at the age of 11 or 12 - and highly correlated with IQ-scores. Data from Statistics Netherlands were used to compare CITO-test scores of 143 apprehended cybercriminals with those of 143 apprehended traditional criminals and 143 non-criminals, matched on age, sex, and country of birth. Ordinary Least Squares regression analyses were used to compare CITO test scores between cybercriminals, traditional criminals, and non-criminals. Additionally, a discordant sibling design was used to control for unmeasured confounding by family factors. Findings reveal that cybercriminals have significantly higher CITO test scores compared to traditional criminals and significantly lower CITO test scores compared to non-criminals.
The shift from home and custom-made clothing to mass-produced ready-mades in 20 th-century Europe has been the subject of studies from multiple disciplines and across various locations. Contributing to this field of studies, and extending the analysis until the present day, a group of female consumers living in Amsterdam in the 1950s and 2010s were interviewed about their sartorial habits. The study identifies a discrepancy between common manufacturing processes and values related to identity as a central cause of this shift. Furthermore it explores how such a discrepancy can be found again today, arguing that this divergence is leading to the re-emergence of customized production.
The project aim is to improve collusion resistance of real-world content delivery systems. The research will address the following topics: • Dynamic tracing. Improve the Laarhoven et al. dynamic tracing constructions [1,2] [A11,A19]. Modify the tally based decoder [A1,A3] to make use of dynamic side information. • Defense against multi-channel attacks. Colluders can easily spread the usage of their content access keys over multiple channels, thus making tracing more difficult. These attack scenarios have hardly been studied. Our aim is to reach the same level of understanding as in the single-channel case, i.e. to know the location of the saddlepoint and to derive good accusation scores. Preferably we want to tackle multi-channel dynamic tracing. • Watermarking layer. The watermarking layer (how to embed secret information into content) and the coding layer (what symbols to embed) are mostly treated independently. By using soft decoding techniques and exploiting the “nuts and bolts” of the embedding technique as an extra engineering degree of freedom, one should be able to improve collusion resistance. • Machine Learning. Finding a score function against unknown attacks is difficult. For non-binary decisions there exists no optimal procedure like Neyman-Pearson scoring. We want to investigate if machine learning can yield a reliable way to classify users as attacker or innocent. • Attacker cost/benefit analysis. For the various use cases (static versus dynamic, single-channel versus multi-channel) we will devise economic models and use these to determine the range of operational parameters where the attackers have a financial benefit. For the first three topics we have a fairly accurate idea how they can be achieved, based on work done in the CREST project, which was headed by the main applicant. Neural Networks (NNs) have enjoyed great success in recognizing patterns, particularly Convolutional NNs in image recognition. Recurrent NNs ("LSTM networks") are successfully applied in translation tasks. We plan to combine these two approaches, inspired by traditional score functions, to study whether they can lead to improved tracing. An often-overlooked reality is that large-scale piracy runs as a for-profit business. Thus countermeasures need not be perfect, as long as they increase the attack cost enough to make piracy unattractive. In the field of collusion resistance, this cost analysis has never been performed yet; even a simple model will be valuable to understand which countermeasures are effective.
