Cyber-Dependent Crime Victimization: The Same Risk for Everyone?

Abstract

The Internet has simplified daily life activities. However, besides its comfortability, the Internet also presents the risk of victimization by several kinds of crimes. The present article addresses the question of which factors influence cyber-dependent crime and how they vary between three kinds of cyber-dependent offences: malware infection, ransomware infection, and misuse of personal data. According to the Routine Activity Approach, it is assumed that crime is determined by a motivated offender, the behavior of the Internet user, and the existence of prevention factors. Our analyses were based on a random sample of 26,665 Internet users in two federal states in Germany, aged 16 years and older; 16.6 percent of the respondents had experienced at least one form of cyber-dependent victimization during the year 2014. The results indicate that individual and household factors, as well as online and prevention behavior, influence the risk of cyber-dependent victimization. Furthermore, the effects differ between the three types of offences. In conclusion, the risk of being victimized by cyber-dependent crime is not the same for anyone, but depends on multivariate factors according to the idea of Routine Activity Approach. However, in view of the fact that crime-related factors also matter, studying different cybercrime offences separately seems to be an appropriate research approach.

Cybercrime Victimisation and Polyvictimisation in Finland—Prevalence and Risk Factors

“…Näsi et al, 2015Näsi et al, , 2017van Wilsem, 2013), and that males have commonly been found to be more likely victims of other types of online offences (e.g. Bergmann et al, 2018;Milani et al, 2020;Näsi et al, 2015). Education and financial status do not appear to be more notable factors regarding online victimisation than they do in relation to offline crime.…”

“…For our analysis, firstly, we provide descriptive statistics of all the victimisation items, secondly, estimate logistic regression models, and thirdly Poisson regression models for the selected outcomes. The estimates from the logistic regression models are reported in average marginal effects (AME) (see also Bergmann et al, 2018). The reason for this is to give a more descriptive picture of the results.…”

“…Burnes et al, 2020;Reyns, 2013;; malware, ransomware, and misuse of personal data (e.g. Bergmann et al, 2018;Holt & Bossler, 2008;Holt et al, 2020;Kigerl, 2021); spam and phishing (e.g. Kigerl, 2012Kigerl, , 2021, online hate (e.g., Kaakinen et al, 2018;Räsänen et al, 2016;Wachs et al, 2021); online harassment (e.g., Näsi et al, 2017;Reyns et al, 2011;van Wilsem, 2011); and online bullying (e.g.…”

“…While we controlled for internet usage in our models, it is also possible that only certain types of online behavioural patterns expose individuals to, for example, harassment or malware, which may explain the observed results (e.g. Bergmann et al, 2018;Branley & Covey, 2017;Reyns et al, 2019). It appears that our measure of user skills behaves similarly to internet use activity.…”

Identifying risky Internet use: Associating negative online experience with specific online behaviours

“…The existing literature on cybercrime and victimisation largely fails to differentiate between distinct types of crimes (Bergmann et al, 2017). Despite the lack of a typology distinguishing cybercrime, risks and consequences, research suggests that various online activities are associated with specific types of cybercrime experiences (Bergmann et al, 2017; Leukfeldt and Yar, 2016). Further research may enable the identification of objectively risky online behaviour profiles based on the association between engagement in an activity and the likelihood of experiencing specific forms of victimisation.…”