Often overlooked in the measurement of crime is the underlying size of offender populations. This holds true for online property crimes involving the sale, purchase, and use of stolen financial data. Though available data suggests that online frauds are steadily increasing, there are currently no estimates of the scope of this offender population. The current study addresses this issue by using capture–recapture methods to estimate the size of the population participating in stolen data markets over a calendar year. Data analysis involved samples collected from three websites that facilitate financial crimes and frauds. Findings suggest that markets are much larger in size than what can otherwise be observed, are heterogeneous, and that buyers outnumber vendors.
AblonL.LibickiM. C.GolayA. A. (2014). Markets for cybercrime tools and stolen data: Hackers’ Bazaar. Santa Monica, CA: RAND.
2.
AfrozS.GargV.McCoyD.GreenstadtR. (2013, September). Honor among thieves: A common’s analysis of cybercrime economies. In 2013 eCrime Researchers Summit (eCRS) (pp. 1-11). Piscataway, NJ: IEEE.
3.
AfrozS.IslamA. C.StolermanA.GreenstadtR.McCoyD. (2014, May). Doppelgänger finder: Taking stylometry to the underground. In 2014 IEEE Symposium on Security and Privacy (pp. 212-226). Piscataway, NJ: IEEE. Retrieved from http://ieeexplore.ieee.org/document/6956566/
BlumsteinA.CohenJ.FarringtonD. P. (1988). Criminal career research: Its value for criminology. Criminology, 26(1), 1-35.
6.
BlumsteinA.CohenJ.RothJ.VisherC. A. (1986). Criminal careers and “career criminals.”Washington, DC: National Academy of Sciences.
7.
BöhningD. (2010). Some general comparative points on Chao’s and Zelterman’s estimators of population size. Scandinavian Journal of Statistics, 37, 221-236.
8.
BöhningD.van der HeijdenP. G. M. (2009). A covariate adjustment for zero-truncated approaches to estimating the size of hidden and elusive populations. Annals of Applied Statistics, 3, 595-610.
9.
BöhningD.Vidal-DiezA.LerdsuwansriR.ViwatwongkasemC.ArnoldM. (2013). A generalization of Chao’s estimator for covariate information. Biometrics, 69, 1033-1042.
10.
BouchardM. (2007). A capture–recapture model to estimate the size of criminal populations and the risks of detection in a marijuana cultivation industry. Journal of Quantitative Criminology, 23, 221-241.
11.
BouchardM.LussierP. (2015). Estimating the size of the sexual aggressor population. In BloklandA.LussierP. (Eds.), Sex offenders: A criminal career approach (pp. 351-371). Hoboken, NJ: Wiley.
12.
BouchardM.TremblayP. (2005). Risks of arrest across drug markets: A capture-recapture analysis of “hidden” dealer and user populations. Journal of Drug Issues, 35, 733-754.
BozdoganH. (1987). Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345-370.
Canadian Anti-Fraud Centre. (2014). Annual Statistical Report 2013: Mass marketing fraud and ID theft activities. North Bay, Ontario, Canada: Author.
18.
Canadian Bankers Association. (2015). Credit card fraud and Interac debit card fraud statistics: Canadian issued cards. Toronto, Ontario, Canada: Author.
19.
ChaoA. (1989). Estimating population size for sparse data in capture–recapture experiments. Biometrics, 45, 427-438.
20.
CollinsM. F.WilsonR. M. (1990). Automobile theft: Estimating the size of the criminal population. Journal of Quantitative Criminology, 6, 395-409.
21.
CoumansA. M.CruyffM. J. L. F.Van der HeijdenP. G. M.WolfJ.SchmeetsH. (2017). Estimating homelessness in the Netherlands using a capture-recapture approach. Social Indicators Research, 130, 189-212.
22.
Décary-HétuD.AldridgeJ. (2015). Shifting through the net: Monitoring of online offenders by researchers. European Review of Organised Crime, 2, 122-141.
23.
Décary-HétuD.LaferrièreD. (2015). Discrediting vendors in online criminal markets. In BichlerG.MalmA. E. (Eds.), Disrupting criminal networks: Network analysis in crime prevention (pp. 129-151). Boulder, CO: FirstForumPress.
24.
FilipkowskiW. (2008). Cyber laundering: An analysis of typology and techniques. International Journal of Criminal Justices Sciences, 3(1), 15-27.
25.
FlorêncioD.HerleyC. (2013). Sex, lies and cyber-crime surveys. In SchneierB. (Ed.), Economics of information security and privacy III (pp. 35-53). New York, NY: Springer.
26.
GreeneM. A. (1984). Estimating the size of a criminal population using an open population approach. In Proceedings of American Statistical Association, Survey Research Methods Section (pp. 8-13).
27.
GreeneM. A.StollmackS. (1981). Estimating the number of criminals. In FoxJ. A. (Ed.), Models of quantitative criminology (pp. 1-24). New York, NY: Academic Press.
28.
HerleyC.FlorêncioD. (2010). Nobody sells gold for the price of silver: Dishonesty, uncertainty and the underground economy. In MooreT.PymD. J.IoannidisC. (Eds.), Economics of information security and privacy (pp. 33-53). New York, NY: Springer.
29.
HoltT. J. (2013). Exploring the social organisation and structure of stolen data markets. Global Crime, 14, 155-174.
30.
HoltT. J.LampkeE. (2010). Exploring stolen data markets online: Products and market forces. Criminal Justice Studies, 23(1), 33-50.
31.
HoltT. J.SmirnovaO.ChuaY. T. (2016). Exploring and estimating the revenues and profits of participants in stolen data markets. Deviant Behavior, 37, 353-367.
32.
HoltT. J.SmirnovaO.ChuaY. T.CopesH. (2015). Examining the risk reduction strategies of actors in online criminal markets. Global Crime, 16, 81-103.
33.
HoltT. J.SmirnovaO.StrumskyD.KilgerM. (2014). Advancing research on hackers through social network data. In MarcumC. D.HigginsG. E. (Eds.), Social networking as a criminal enterprise (pp. 145-167). Boca Raton, FL: CRC Press.
34.
HutchingsA.HoltT. J. (2015). A crime script analysis of the online stolen data market. British Journal of Criminology, 55, 596-614.
35.
KendallL. W. (1999). Robustness of closed capture-recapture methods to violations of the closure assumption. Ecology, 80, 2517-2525.
LoughranT. A.NguyenH.PiqueroA. R.FaganJ. (2013). The returns to criminal capital. American Sociological Review, 78, 925-948.
38.
LusthausJ. (2012). Trust in the world of cybercrime. Global Crime, 13, 71-94.
39.
MacdonaldM.FrankR.MeiJ.MonkB. (2015, August). Identifying digital threats in a hacker web forum. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 926-933). Piscataway, NJ: IEEE.
MotoyamaM.McCoyD.LevchenkoK.SavageS.VoelkerG. M. (2011). An analysis of underground forums. In Proceedings of the 2011 ACM SIGCOMM Conference (pp. 71-80). Retrieved from http://cseweb.ucsd.edu/~mmotoyam/imc11-forums.pdf
42.
NagurneyA. (2015). A multiproduct network economic model of cybercrime in financial services. Service Science, 7(1), 70-81.
PreeceJ.NonneckeB.AndrewsD. (2004). The top five reasons for lurking: Improving community experiences for everyone. Computers in Human Behavior, 20, 201-223.
45.
RossmoD. K.RoutledgeR. (1990). Estimating the size of criminal populations. Journal of Quantitative Criminology, 6, 293-314.
46.
SoudijnM. R. J.ZegersB. C. H. T. (2012). Cybercrime and virtual offender converge settings. Trends in Organized Crime, 15, 111-129.
47.
SparrowM. K. (1991). The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks, 13, 251-274.
48.
TcherniM.DaviesA.LopesG.LizotteA. (2016). The dark figure of online property crime: Is cyberspace hiding a crime wave?Justice Quarterly, 33, 890-911. doi:10.1080/07418825.2014.994658
49.
van der HeijdenP. G. M.CrutsG.CruyffM. (2013). Methods for population size estimation of problem drug users using a single registration. International Journal of Drug Policy, 24, 614-618. doi:10.1016/j.drugpo.2013.04.002
50.
van der HeijdenP. G. M.CruyffM.BöhningD. (2014). Capture recapture to estimate criminal populations. In Encyclopedia of criminology and criminal justice (pp. 267-276). New York, NY: Springer.
51.
WilliamsM. L.BurnapP.SloanL. (2017). Crime sensing with big data: The affordances and limitations of using open-source communications to estimate crime patterns. The British Journal of Criminology, 57(2), 320-340.
52.
WilliamsM.LeviM. (2012). Perceptions of the eCrime controllers: Modelling the influence of cooperation and data source factors. Security Journal, 28, 252-271. doi:10.1057/sj.2012.47
53.
World Bank. (2016). Internet users (per 100 people). Washington, DC: Author.
54.
YipM.ShadboltN.WebberC. (2012, June). Structural analysis of online criminal social networks. In 2012 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 60-65). Piscataway, NJ: IEEE.
55.
ZeltermanD. (1988). Robust estimation in truncated discrete distributions with application to capture-recapture experiments. Journal of Statistical Planning and Inference, 18, 225-237.
