Do police crackdowns disrupt drug cryptomarkets? a longitudinal analysis of the effects of Operation Onymous

In recent years, there has been a proliferation of online illicit markets where participants can purchase and sell a wide range of goods and services such as drugs, hacking services, and stolen financial information. Second-generation markets, known as cryptomarkets, provide a pseudo-anonymous platform from which to operate and have attracted the attention of researchers, regulators, and law enforcement. This paper focuses on the impact of police crackdowns on cryptomarkets, and more particularly on the impact of Operation Onymous, a large-scale police operation in November 2014 that targeted many cryptomarkets. Our results demonstrate that cryptomarket participants adapt to police operations and that the impact of Operation Onymous was limited in time and scope. Of particular interest is the finding that prices did not increase following Operation Onymous, even though many dealers retired shortly after it occurre

How do illicit drugs move across countries? A network analysis of the heroin supply to Europe

Illicit drugs are trafficked across manifold borders before ultimately reaching consumers. Consequently, interdiction of cross-border drug trafficking forms a critical component of the European Union’s initiative to reduce drug supplies. However, there is contradictory evidence about its effectiveness, which is due, in part, to a paucity of information about how drugs flow across borders. This study uses a network approach to analyse international drug trafficking both to and within Europe, drawing on several perspectives to delineate the factors that affect how drug shipments move across borders. The analysis explicates how drug trafficking is concentrated along specific routes; moreover, we demonstrate that its structure is not random, but, rather, driven by specific factors. In particular, corruption, social and geographical proximity are key factors explaining the configuration of heroin supply to European countries. This study also provides essential insights into the disruption of traffickers’ illicit activities

Identifying human trafcking indicators in the UK online sex market

This study identifes the presence of human trafcking indicators in a UK-based sample of sex workers who advertise their services online. To this end, we developed a crawling and scraping software that enabled the collection of information from 17, 362 advertisements for female sex workers posted on the largest dedicated platform for sex work services in the UK. We then established a set of 10 indicators of human trafcking and a transparent and replicable methodology through which to detect their presence in our sample. Most of the advertisements (58.3%) contained only one indicator, while 3,694 of the advertisements (21.3%) presented 2 indicators of human trafcking. Only 1.7% of the advertisements reported three or more indicators, while there were no advertisements that featured more than four. 3, 255 advertisements (19.0%) did not contain any indicators of human trafcking. Based on this analysis, we propose that this approach constitutes an efective screening process for quickly identifying suspicious cases, which can then be examined by more comprehensive and accurate tools to identify if human trafcking is occurring. We conclude by calling for more empirical research into human trafcking indicators

The Italian mafias in the world: a systematic assessment of the mobility of criminal groups

This study complements existing literature on the mobility of criminal groups (mainly based on country case studies) with the first systematic assessment of the worldwide activities of the four main types of Italian mafias (Cosa Nostra, Camorra, ’Ndrangheta and Apulian mafias) from 2000 to 2012. Drawing from publicly available reports, a specific multiple correspondence analysis identifies the most important associations among mafias, activities, and countries. The results show that the mafias concentrate in a few countries; drug trafficking is the most frequent activity, whereas money laundering appears less important than expected; a stable mafia presence is reported in a few developed countries (mainly Germany, Canada, Australia, and the United States). The mafias show significant differences: the ’Ndrangheta tends to establish structured groups abroad, whereas the other mafias mainly participate in illicit trades

Interdicting international drug trafficking: a network approach for coordinated and targeted interventions

There is a relative dearth of literature on both the effects of cross-border interdictions and the impact of different types of interventions on international drug trafficking. This study identifies the main trafficking routes for cocaine and heroin, along with comparing the disruptive effects induced by targeted and non-coordinated interventions. It adopts a social network approach to identify the routes along which cocaine and heroin are trafficked, and then simulates the impact of different interdiction strategies on these two trafficking networks. The findings indicate that targeting countries based on their respective positions in the networks, as opposed to on the basis of the quantity of drugs exchanged, is more likely to disrupt drug flows. More specifically, concentrating law enforcement resources on countries with several incoming or outgoing trafficking connections, or those countries that mediate between producer, transit and consumer countries, would appear to be particularly effective in this regard. Interventions focused on specific trafficking routes are also likely to be effective if these routes have high edge betweenness centrality scores. This study contributes to extant understanding on the vulnerability of cocaine and heroin international trafficking networks, and, moreover, demonstrates that empirically-driven strategies are potentially more effective at interdicting international trafficking than non-strategic and non-coordinated interventions

Exploring the perils of cross national comparisons of drug prevalence: the effect of survey modality

Background: There is significant interest in comparing countries on many different indicators of social problems and policies. Cross-national comparisons of drug prevalence and policies are often hampered by differences in the approach used to reach respondents and the methods used to obtain information in national surveys. The paper explores how much these differences could affect cross-country comparisons. Methods: This study reports prevalence of drug use according to the most recent national household survey and then adjusts estimates as if all national surveys used the same methodology. It includes in the analysis European countries for which the European Monitoring Centre for Drugs and Drug Addiction provides the data, the United States, Canada, and Australia. Adjustment factors are based on US data. Findings: Adjusting for modality differences appears likely to modestly affect the rankings of countries by prevalence, but to an extent that could be important for comparisons. For example, general population surveys suggest that the US had some of the highest cannabis and cocaine prevalence rates circa 2012, but this is partially driven by the use of a modality known to produce higher prevalence estimates. This analysis shows that country rankings are partly an artifact of the mode of interview used in national general population surveys. Conclusions: Our preliminary efforts suggest that cross-national prevalence comparisons, policy analyses and other projects such as estimating the global burden of disease could be improved by adjusting estimates from drug use surveys for differences in modality. Research is needed to create more authoritative adjustment factors

The determinants of heroin flows in Europe: a latent space approach

This study utilises recent advances in statistical models for social networks to identify the factors shaping heroin trafficking in relation to European countries. First, it estimates the size of the heroin flows among a network of 61 countries, before subsequently using a latent space approach to model the presence of trafficking and the amount of heroin traded between any two given countries. Many networks, such as trade networks, are intrinsically weighted, and ignoring edge weights results in a loss of relevant information. Traditionally, the gravity model has been used to predict legal trade flows, assuming conditional independence among observations. More recently, latent space position models for social networks have been used to analyze legal trade among countries, and, mutatis mutandis, can be applied to the context of illegal trade to count both edge weights and conditional dependence among observations. These models allow for a better understanding of the generative processes and potential evolution of heroin trafficking routes. This study shows that geographical and social proximity provide fertile ground for the formation of heroin flows. Opportunities are also a driver of drug flows towards countries where regulation of corruption is weak

Online and offline determinants of drug trafficking across countries via cryptomarkets

Drug cryptomarkets are a significant development in the recent history of illicit drug markets. Dealers and buyers can now finalize transactions with people they have never met, who could be located anywhere across the globe. What factors shape the geography of international drug trafficking via these cryptomarkets? In our current study, we test the determinants of drug trafficking through cryptomarkets by using a mix of social network analysis and a new dataset composed of self-reported transactions. Our findings contribute to existing research by demonstrating that a country’s level of technological advancement increases the probability of forming trafficking connections on cryptomarkets. Additionally, we found that a country’s capacity to police cryptomarkets reduces the number of trafficking connections with other countries. We also observed that trafficking on cryptomarkets is more likely to occur between countries that are geographically close. In summary, our study highlights the need to consider both online and offline factors in research on cryptomarkets

Digital fingerprinting for identifying malicious collusive groups on Twitter

Propagation of malicious code on online social networks (OSN) is often a coordinated effort by collusive groups of malicious actors hiding behind multiple online identities (or digital personas). Increased interaction in OSN have made them reliable for the efficient orchestration of cyber-attacks such as phishing click bait and drive-by downloads. URL shortening enables obfuscation of such links to malicious websites and massive interaction with such embedded malicious links in OSN guarantees maximum reach. These malicious links lure users to malicious endpoints where attackers can exploit system vulnerabilities. Identifying the organised groups colluding to spread malware is non-trivial owing to the fluidity and anonymity of criminal digital personas on OSN. This paper proposes a methodology for identifying such organised groups of criminal actors working together to spread malicious links on OSN. Our approach focuses on understanding malicious users as ‘digital criminal personas’ and characteristics of their online existence. We first identify those users engaged in propagating malicious links on OSN platforms, and further develop a methodology to create a digital fingerprint for each malicious OSN account/digital persona. We create similarity clusters of malicious actors based on these unique digital fingerprints to establish ‘collusive’ behaviour. We evaluate the ability of a cluster-based approach on OSN digital fingerprinting to identify collusive behaviour in OSN by estimating within-cluster similarity measures and testing it on a ground truth dataset of five known colluding groups on Twitter. Our results show that our digital fingerprints can identify 90% of cyber-personas engaged in collusive behaviour 75% of collusion in a given sample set

Disrupting networks of hate: Characterising hateful networks and removing critical nodes

Hateful individuals and groups have increasingly been using the Internet to express their ideas, spread their beliefs, and recruit new members. Under- standing the network characteristics of these hateful groups could help understand individuals’ exposure to hate and derive intervention strategies to mitigate the dangers of such networks by disrupting communications. This article analyses two hateful followers net- works and three hateful retweet networks of Twitter users who post content subsequently classified by hu- man annotators as containing hateful content. Our analysis shows similar connectivity characteristics between the hateful followers networks and likewise between the hateful retweet networks. The study shows that the hateful networks exhibit higher connectivity characteristics when compared to other ”risky” networks, which can be seen as a risk in terms of the likelihood of expo- sure to, and propagation of, online hate. Three network performance metrics are used to quantify the hateful content exposure and contagion: giant component (GC) size, density and average shortest path. In order to efficiently identify nodes whose removal reduced the flow of hate in a network, we propose a range of structured node-removal strategies and test their effectiveness. Results show that removing users with a high degree is most effective in reducing the hateful followers network connectivity (GC, size and density), and therefore reducing the risk of exposure to cyberhate and stemming its propagation