This article examines the network structure, criminal cooperation, and external interactions of cybercriminal networks. Its contribution is empirical and inductive. The core of this study involved carrying out 10 case analyses on closed cybercrime investigations – all with financial motivations on the part of the offenders - in the UK and beyond. Each analysis involved investigator interview and access to unpublished law enforcement files. The comparison of these cases resulted in a wide range of findings on these cybercriminal networks, including: a common division between the scam/attack components and the money components; the presence of offline/local elements; a broad, and sometimes blurred, spectrum of cybercriminal behaviour and organisation. An overarching theme across the cases that we observe is that cybercriminal business models are relatively stable.
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ENGLISH: A vast and growing body of research has shown that crime tends to run in families. However, previous studies focused only on traditional crimes and research on familial risk factors for cyber offending is very scarce. To address this gap in the literature, the present study examines the criminal behavior of the family members of a sample of cyber offenders prosecuted in the Netherlands. The sample consists of 979 cyber offenders prosecuted for computer trespassing between 2001 and 2018, and two matched groups of 979 traditional offenders and 979 non-offenders. Judicial information and kinship data from Dutch Statistics were used to measure criminal behavior among family members. Both traditional offenders and cyber offenders were found to be more likely to have criminal fathers, mothers, and siblings than non-offenders. Additional analyses, however, showed different patterns between cyber offenders who were only prosecuted for cyber offenses and those who also committed traditional crimes. While the former group of cyber offenders were similar to non-offenders in terms of family offending, the latter group of cyber offenders were more similar to traditional offenders. Overall, these results suggest that the traditional mechanisms of intergenerational transmission of crime can only partially explain cybercrime involvement. NEDERLANDS: Uit een groot en groeiend aantal onderzoeken blijkt dat criminaliteit vaak in families voorkomt. Eerdere studies richtten zich echter alleen op traditionele misdrijven en onderzoek naar familiaire risicofactoren voor cybercriminaliteit is zeer schaars. Om deze leemte in de literatuur op te vullen, onderzoekt deze studie het criminele gedrag van familieleden van een steekproef van cyberdelinquenten die in Nederland worden vervolgd. De steekproef bestaat uit 979 cyberdelinquenten die tussen 2001 en 2018 zijn vervolgd voor computervredebreuk, en twee gematchte groepen van 979 traditionele delinquenten en 979 niet-delinquenten. Justitiële informatie en verwantschapsgegevens van het Centraal Bureau voor de Statistiek werden gebruikt om crimineel gedrag onder familieleden te meten. Zowel traditionele daders als cybercriminelen bleken vaker criminele vaders, moeders en broers en zussen te hebben dan niet-daders. Aanvullende analyses lieten echter verschillende patronen zien tussen cyberdelinquenten die alleen werden vervolgd voor cyberdelicten en degenen die ook traditionele delicten pleegden. Terwijl de eerste groep cyberdelinquenten vergelijkbaar was met niet-delinquenten wat betreft gezinsdelinquentie, leek de tweede groep cyberdelinquenten meer op traditionele delinquenten. In het algemeen suggereren deze resultaten dat de traditionele mechanismen van intergenerationele overdracht van criminaliteit de betrokkenheid bij cybercriminaliteit slechts gedeeltelijk kunnen verklaren.
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The first reason for this study is that a gap appears to exist in the available theories on corruption; very little research is available on corruption by a network, nor does the network theory thoroughly discuss the risks or pitfalls of networks. The second reason for this study is the observation that policies and investigations appear to be limited in dealing with corruption in network-like structures, while at the same time international and European organisations refer to ‘trading in influence’, political and high-profile corruption and the need to eradicate these forms of corruption. The descriptions of both trading in influence and ‘political corruption’ are such that they refer to a web, circle or network in which corruption occurs. The current literature and research on corruption in network-like structures is not extensive, nor do literature and research on networks extensively discuss the pitfalls of networks or how such a collective can become corrupt. This study seeks to bridge both themes, thereby learning from the day-to-day reality of large corruption cases which are difficult to investigate, and comparing them to what is described in the existing bodies of literature on corruption and networks. Thus, understanding network corruption is relevant to prevent, detect and address corruption in our modern society. It will help create awareness and understanding of when social capital becomes corrupted. In this study I consider the structure of the network and the responsibility in these networks in real-life cases: what consequences this has in terms of how the conduct should be assessed; what it means when corruption is a collective behaviour; and whether the behaviour of individuals can be assessed independently of the network. We need to understand which norms are laid down in the anti-corruption policies and models and compare them with the norms upheld in social networks, to explore whether it is possible to distinguish networks from network corruption. In particular, the various roles that individuals take on in such networks needs consideration. The three case studies (the FIFA case, the News of the World case and the Roermond case) concerned serious misconduct by a given set of persons having some form of loose association, but sometimes with little individual behaviour being criminal. In this study the link between networks and corruption is explored by means of the following central question: In what way and to what extent is corruption linked to the functioning of social networks, and what does this entail for our knowledge of corruption and networks and the policies to eradicate corruption?
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Despite their various appealing features, drones also have some undesirable side-effects. One of them is the psychoacoustic effect that originates from their buzzing noise that causes significant noise pollutions. This has an effect on nature (animals run away) and on humans (noise nuisance and thus stress and health problems). In addition, these buzzing noises contribute to alerting criminals when low-flying drones are deployed for safety and security applications. Therefore, there is an urgent demand from SMEs for practical knowledge and technologies that make existing drones silent, which is the main focus of this project. This project contributes directly to the KET Digital Innovations\Robotics and multiple themes of the top sectors: Agriculture, Water and Food, Health & Care and Safety. The main objective of this project is: Investigate the desirability and possibilities of extremely silent drone technologies for agriculture, public space and safety This is an innovative project and there exist no such drone technology that attempts to reduce the noises coming from drones. The knowledge within this project will be converted into the first proof-of-concepts that makes the technology the first Minimum Viable Product suitable for market evaluations. The partners of this project include WhisperUAV, which has designed the first concept of a silent drone. As a fiber-reinforced 3D composite component printer, Fiberneering plays a crucial role in the (further) development of silent drone technologies into testable prototypes. Sorama is involved as an expert company in the context of mapping the sound fields in and around drones. The University of Twente is involved as a consultant and co-developer, and Research group of mechatronics at Saxion is involved as concept developer, system and user requirement verifier and validator. As an unmanned systems innovation cluster, Space53 will be involved as innovation and networking consultant.
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
