Abstract
It is unknown to what extent cyber-dependent offenders are distinctly different from other offenders and to what extent they have different motives. This is addressed in this study by examining to what extent cyber-dependent offenders can be distinguished from traditional offenders and by identifying clusters of cyber-dependent and traditional offenses. In addition, it is explored which motives for offending the offenders provide and to what extent a specific cluster of crimes distinguishes itself from the other clusters by specific motives. The analyses are based on a survey among a Dutch high-risk sample of adult cyber-dependent offenders (N = 268) and traditional offenders (N = 270). The principal component analysis identified seven clusters of crimes, four clusters that include only cyber-dependent crime and three clusters that only include traditional crimes. This indicates that cyber-dependent offenders can be distinguished from traditional offenders. In addition, cyber-dependent crimes can be distinguished from traditional crimes by almost all motives. The cyber-dependent crimes are mostly committed out of intrinsic motives, i.e., committing the crime is in itself rewarding. Financial motives are almost absent for cyber-dependent crime. Differences between cyber-dependent crime clusters are mainly found in extrinsic motives, i.e., the extent to which the external consequences of committing a crime is rewarding. The results are discussed in light of the existing cybercrime literature.
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Notes
- 1.
It should be noted that not all literature about hackers is necessarily only about criminals. Hacking can be part of a completely legitimate profession.
- 2.
This procedure was approved by the Ethics Committee. Apart from selecting the respondents, the Public Prosecutor’s Office was not involved in sending the letters or the rest of the data collection and analyses. Respondents could, therefore, participate anonymously, and their personal results would not be shared with the Public Prosecutor’s Office.
- 3.
Communication with this type of website is completely encrypted and less easy to trace. Three traditional sample respondents completed the survey on paper and three cyber-dependent sample respondents completed it through the Tor Hidden Service website.
- 4.
Some also load on another cluster (factor loading above 0.30). For interpretation clarity and further analyses, the highest factor loading is used to assign each crime to only one cluster.
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Appendices
Appendix 1: Pattern Matrix Principal Component Analysis
Factor | |||||||
|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | |
(α = 0.75) | (α = 0.62) | (α = 0.60) | (α = 0.74) | (α = 0.63) | (α = 0.66) | (α = 0.59) | |
Cybercrime factor C1 | Traditional crime factor T1 | Cybercrime factor C2 | Cybercrime factor C3 | Traditional crime factor T2 | Traditional crime factor T3 | Cybercrime factor C4 | |
Guessing password | 0.70 | 0.03 | −0.08 | 0.10 | 0.08 | −0.09 | 0.07 |
Digital theft | 0.77 | −0.10 | 0.14 | −0.03 | −0.11 | 0.07 | 0.05 |
Hacking | 0.65 | 0.18 | −0.13 | 0.35 | −0.18 | −0.06 | 0.06 |
Damaging data | 0.65 | 0.05 | 0.07 | 0.10 | 0.12 | 0.03 | −0.09 |
Tax fraud | 0.00 | 0.77 | 0.09 | 0.09 | −0.27 | 0.06 | −0.02 |
Stolen goods | 0.07 | 0.63 | −0.02 | −0.06 | 0.22 | 0.09 | −0.03 |
Insurance fraud | −0.01 | 0.71 | 0.12 | −0.03 | 0.11 | −0.14 | 0.08 |
Defacing | −0.07 | 0.11 | 0.47 | 0.14 | −0.21 | 0.34 | 0.11 |
Phishing | 0.38 | −0.07 | 0.50 | −0.07 | 0.04 | 0.17 | 0.19 |
DDoS | −0.04 | 0.16 | 0.61 | 0.26 | 0.12 | −0.02 | 0.03 |
Spam | 0.08 | 0.13 | 0.78 | −0.08 | 0.09 | −0.21 | 0.00 |
Taking control | 0.19 | −0.01 | 0.03 | 0.83 | 0.03 | 0.08 | −0.07 |
Intercepting communication | 0.21 | 0.01 | −0.04 | 0.66 | 0.34 | −0.11 | 0.00 |
Vandalism | 0.09 | 0.14 | 0.29 | 0.07 | 0.37 | 0.26 | −0.23 |
Burglary | −0.06 | 0.20 | 0.22 | 0.25 | 0.63 | −0.12 | 0.09 |
Using weapon | 0.04 | 0.02 | 0.02 | 0.12 | 0.71 | 0.18 | 0.19 |
Stealing | 0.35 | 0.25 | 0.14 | −0.08 | 0.05 | 0.40 | −0.14 |
Threats | −0.01 | −0.05 | 0.02 | 0.07 | −0.10 | 0.75 | 0.11 |
Violence | −0.01 | 0.12 | −0.15 | −0.10 | 0.35 | 0.54 | 0.28 |
Carry weapon | 0.10 | −0.17 | 0.20 | 0.00 | 0.25 | 0.50 | −0.26 |
Selling drugs | −0.04 | 0.35 | −0.24 | 0.05 | 0.08 | 0.54 | −0.06 |
Malware | −0.14 | −0.02 | 0.14 | 0.48 | −0.11 | 0.15 | 0.55 |
Selling data | 0.43 | 0.01 | −0.04 | −0.11 | 0.04 | −0.08 | 0.64 |
Selling credentials | −0.01 | 0.05 | 0.18 | 0.03 | 0.27 | 0.09 | 0.70 |
Appendix 2: Evidence for Significant Differences in Motives Between Clusters
These tables are based on clustered (respondent-crime) multivariate probit models. The underlying parameter estimates are available upon request. Dark gray areas show comparisons between a specific cybercrime and traditional crime cluster, while light gray areas show comparisons between a specific cybercrime and another cybercrime cluster, or a specific traditional crime and another traditional crime cluster.
IM: Intrinsic motives | |||||||||||||||
IM1: Boredom/curiosity/excitement | IM3: Challenging/educational | ||||||||||||||
C1 | C2 | C3 | C4 | T1 | T2 | T3 | C1 | C2 | C3 | C4 | T1 | T2 | T3 | ||
C1 | (+) | +++ | (+) | +++ | C1 | + | + | ||||||||
C2 | (−) | (+) | C2 | ||||||||||||
C3 | (+) | C3 | (+) | ||||||||||||
C4 | ++ | + | C4 | ||||||||||||
T1 | – – – | (−) | (−) | – – | T1 | − | (−) | ||||||||
T2 | (−) | T2 | |||||||||||||
T3 | – – – | − | T3 | − | |||||||||||
IM2: Fun/felt good | IM4: See how far I could go | ||||||||||||||
C1 | C2 | C3 | C4 | T1 | T2 | T3 | C1 | C2 | C3 | C4 | T1 | T2 | T3 | ||
C1 | C1 | ||||||||||||||
C2 | C2 | ||||||||||||||
C3 | − | − | (−) | C3 | |||||||||||
C4 | + | C4 | |||||||||||||
T1 | + | T1 | |||||||||||||
T2 | (+) | T2 | |||||||||||||
T3 | T3 | ||||||||||||||
EM: Extrinsic motives | |||||||||||||||
EM1: Damage something | EM3: Put things straight/deliver a message | ||||||||||||||
C1 | C2 | C3 | C4 | T1 | T2 | T3 | C1 | C2 | C3 | C4 | T1 | T2 | T3 | ||
C1 | +++ | +++ | − | C1 | +++ | ||||||||||
C2 | +++ | +++ | + | C2 | +++ | ||||||||||
C3 | – – – | – – – | – – – | – – – | – – – | C3 | +++ | (−) | |||||||
C4 | – – – | – – – | – – – | – – – | – – – | C4 | – – – | – – – | – – – | – – – | – – – | – – – | |||
T1 | − | +++ | +++ | − | (−) | T1 | +++ | − | |||||||
T2 | + | +++ | +++ | + | T2 | +++ | |||||||||
T3 | +++ | +++ | (+) | T3 | (+) | +++ | + | ||||||||
EM2: Revenge/anger/to bully | EM4: Impress others/gain power | ||||||||||||||
C1 | C2 | C3 | C4 | T1 | T2 | T3 | C1 | C2 | C3 | C4 | T1 | T2 | T3 | ||
C1 | − | – – | C1 | (+) | |||||||||||
C2 | + | ++ | + | C2 | (+) | ||||||||||
C3 | – – | – – | – – | C3 | (−) | (−) | (−) | (−) | |||||||
C4 | − | C4 | |||||||||||||
T1 | − | (−) | – – | T1 | |||||||||||
T2 | ++ | (+) | T2 | (+) | |||||||||||
T3 | ++ | ++ | + | ++ | T3 | (+) | |||||||||
FM: Financial motive | |||||||||||||||
FM: Earn something | |||||||||||||||
C1 | C2 | C3 | C4 | T1 | T2 | T3 | |||||||||
C1 | – – – | − | |||||||||||||
C2 | – – – | – – | |||||||||||||
C3 | – – – | − | |||||||||||||
C4 | – – – | ||||||||||||||
T1 | +++ | +++ | +++ | +++ | +++ | +++ | |||||||||
T2 | – – – | ||||||||||||||
T3 | + | ++ | + | – – – | |||||||||||
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Weulen Kranenbarg, M. (2021). Cyber-Dependent Crime Versus Traditional Crime: Empirical Evidence for Clusters of Offenses and Related Motives. In: Weulen Kranenbarg, M., Leukfeldt, R. (eds) Cybercrime in Context. Crime and Justice in Digital Society, vol I. Springer, Cham. https://doi.org/10.1007/978-3-030-60527-8_12
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