Der Anstieg der Opfererfahrungen durch Internetkriminalität unterstreicht die Notwendigkeit zu verstehen, wie sich Menschen online verhalten und wie unsicheres Online-Verhalten mit Viktimisierung zusammenhängen kann. Bisherige Studien haben sich oft auf selbstberichtete Verhaltensweisen oder Einstellungen zu vorsichtigem Online-Verhalten verlassen. Studien, die sowohl das tatsächliche Online-Verhalten als auch erklärende Faktoren in einer grossen Stichprobe gemessen haben, sind rar. In diesem Beitrag wird das Forschungsinstrument der Online Behaviour and Victimization Study vorgestellt. Das Kapitel skizziert die Entwicklung dieses Instruments, das ein bevölkerungsbasiertes Befragungsexperiment verwendet. Mit diesem Instrument kann das tatsächliche Verhalten von Internetnutzern gemessen werden. Während des Ausfüllens der Umfrage werden die Befragten mit (fiktiven) Cyber-Risikosituationen konfrontiert, wodurch die Forscher analysieren können, wie die Befragten mit diesen Situationen umgehen. Darüber hinaus wurden auf der Grundlage von Theorien und einer umfangreichen Literaturstudie, die in diesem Beitrag kurz skizziert wird, Messungen für zahlreiche erklärende Faktoren in die Studie aufgenommen, darunter Wissen (Bewusstsein), Gelegenheit und Motivation. Schließlich wird die frühere Viktimisierung durch Cyberkriminalität gemessen, was es ermöglicht, den Zusammenhang zwischen dem tatsächlichen Online-Verhalten und der Online-Viktimisierung zu untersuchen.
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Based on the results of two research projects from the Netherlands, this paper explores how street oriented persons adapt and use digital technologies by focusing on the changing commission of instrumental, economically motivated, street crime. Our findings show how social media are used by street offenders to facilitate or improve parts of the crime script of already existing criminal activities but also how street offenders are engaging in criminal activities not typically associated with the street, like phishing and fraud. Taken together, this paper documents how technology has permeated street life and contributed to the ‘hybridization’ of street offending in the Netherlands—i.e. offending that takes place in person and online, often at the same time.
Although the prevalence of cybercrime has increased rapidly, most victims do not report these offenses to the police. This is the first study that compares associations between victim characteristics and crime reporting behavior for traditional crimes versus cybercrimes. Data from four waves of a Dutch cross-sectional population survey are used (N = 97,186 victims). Results show that cybercrimes are among the least reported types of crime. Moreover, the determinants of crime reporting differ between traditional crimes and cybercrimes, between different types of cybercrime (that is, identity theft, consumer fraud, hacking), and between reporting cybercrimes to the police and to other organizations. Implications for future research and practice are discussed. doi: https://doi.org/10.1177/1477370818773610 This article is honored with the European Society of Criminology (ESC) Award for the “Best Article of the Year 2019”. Dit artikel is bekroond met de European Society of Criminology (ESC) Award for the “Best Article of the Year 2019”.
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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.
