Presentation Transcript

1. Modus Operandi

   Marianne Junger [Mon13] A. L. Montoya Morales, M. Junger, and P. H. Hartel. How 'digital' is traditional crime? In European Intelligence and Security Informatics Conference (EISIC), Uppsala, Sweden, Aug 2013. IEEE Computer Society. http://eprints.eemcs.utwente.nl/23423/ Cyber-crime science
2. Origins of CRIME Why do people commit crimes? What aspects play a role? Cyber-crime science
3. Background Crime Science Crime is the product of the environment Independent of personal characteristics Fact Since WWII increase in wealth, more leisure time, higher education. But what happened to crime? Cyber-crime science
4. Development of registered crime 1960-1995 in NL (CBS) 4 Cyber-crime science
5. Why did crime increase? More targets Less supervision Increased mobility Aim of Crime Science = prevention Cyber-crime science
6. Issue today Does digitalization lead to increase in crime? Cyber-crime science
7. Digitalization in he Netherlands 93% of Dutch population is connected to the internet (CBS) 50% also accesses internet via mobile device (smart-phone: 43%, laptop: 21%) 53% is active on social media 79% shop online, 55% are frequent online shoppers Cyber-crime science
8. First expectation Cybercrime is increasing as a result of increasing use of ICT Cyber-crime science
9. Not supported by previous work [Dom09] concluded that cybercrime is ‘at most 1% of all reported crime’ Hollands-Midden: 0.32% of all crime Zuid-Holland-Zuid: 0.54% of all crime [Dom09] M. M. L. Domenie, E. R. Leukfeldt, M. H. Toutenhoofd-Visser, and W. Ph. Stol. Werkaanbod cybercrime bij de politie. eenverkennendonderzoeknaar de omvang van het geregistreerdewerkaanbod cybercrime. Cyren rapport, NHL Hogeschool, Leeuwarden, 2009. Cyber-crime science
10. Previous work [Dom09] followed special methodology [Dom09] measured prevalence in Zuid Holland Zuid and HollandsMidden Definition: “the use of IT for committing criminal activities against persons, property, organizations or electronic communication networks and information systems” Operationalization: Searched for keywords associated with cybercrime, such as "computer", "cyber" or “digital“, using a digital search protocol Findings: 0.32 - 0.54% of all crime reported to the Dutch police constitutes cybercrime in 2 police regions. Cyber-crime science
11. AimUtwentestudy Check these figures following new methodology Our approach is to manually look into the digital modus operandi (MO) of traditional crime Cyber-crime science
12. Second expectation Changes in technology affect characteristics of crime, type of offenders and type of victims Cyber-crime science
13. Previous work does not support this expectation Cybercriminals are younger but basically the same as offenders from traditional crimes [Leu11] [Leu11] E. R. Leukfeldt and W. Ph. Stol. De marktplaatsfraudeurontmaskerd. internetfraudeursvergeleken met klassiekefraudeurs. Secondant, 25(5):26-31, 2011. http://www.hetccv.nl/binaries/content/assets/ccv/secondant/2011/secondant2011-6.pdf. Cyber-crime science
14. Characteristics of cybercriminals [UNO13] UNODC. Comprehensive Study on Cybercrime. United Nations Office on Drugs and Crime, Feb 2013. http://www.unodc.org/documents/organized-crime/UNODC_CCPCJ_EG.4_2013/CYBERCRIME_STUDY_210213.pdf. Cyber-crime science
15. Expectations Cybercrime should increase as society goes online Check figures [Dom09] with new methodology? Digitalisation should affect the characteristics of the type of crime and the type of offenders Do we see changes in cybercrime corresponding to the [UNO13] findings? Aim present study not measure ‘cybercrime’ but penetration of Information and Communication Technology (ICR) in traditional crime Cyber-crime science
16. Method Careful reading of police records (ProcesVerbaal) using a tailor-made checklist Random selection of 900 incidents in Gelderland and Overijssel Crime types: Residential & commercial burglary (n=300) (link to cybercrime is unknown) Threats (n=300) (suspected link to cybercrime) Frauds (suspected link to cybercrime) (n=300) Cyber-crime science
17. Method (Contd.) Crime script Amount of ICT used during Commission of crime (i.e. modus operandi) Criminal investigation Apprehension Cyber-crime science
18. Method (Contd.) Socio-demographic variables, age, sex, place of birth Organized crime measured indirectly: organized crime implies – in the present study Having a criminal record More than a single offender Not having a legal occupation Geographic location: international crime Cyber-crime science
19. Question How much ICT is there in traditional crime? Selection: all cases Cyber-crime science
20. ICT is important for threats and fraud * #24 Unsolicited email sent #30 Threat digital #34 Forgery digital #39 Burglary prior to the offense in digital form Burglary: 1.5% takes place after the commission of the burglary (theft of money via stolen bank cards) * Significant p < .001 Cyber-crime science
21. ICT is important for threats and fraud Threat digital Verbal threats via SMS, MSN Whatsapp, email or on social media Also: denigrating messages or films on YouTube, personal, or business (bad publicity) Digital Fraud Online shopping; ‘E-Bay (Marktplaats) fraud Internet banking: skimming or hacking of bank system Cyber-crime science
22. Characteristics of digital crime Offense Offenders Selection of threats and fraud Cyber-crime science
23. Age: % 34 and younger [UNO13] up to 79% younger than 30 Offender: offenders of digital crimes are older – for fraud (but ns) Cyber-crime science
24. Sex: % female offenders [UNO13] Males: 80% or more Cyber-crime science
25. Role criminal organisation: % cases with only one suspect [UNO13] Cybercrime requires high degree of organization and specialization, at least in financial-driven crimes, up to 90% organized (financially motivated crime) Cyber-crime science
26. Role (contd).: % cases with suspects with a criminal record Cyber-crime science
27. Role (contd.): % cases with suspects with a paid job Cyber-crime science
28. Role (contd.): % cases suspects born in NL Cyber-crime science
29. Geographical distance between the offender and the victim at the time of the crime, in % ** p < 0.01 Cyber-crime science
30. comparison of traditional & digital crime -> normalization Cyber-crime science
31. Criminal Investigation (%)* * % not mutually exclusive Cyber-crime science
32. Importance of tools for apprehension * Significant: p < .05; ** Significant: p < .01 Cyber-crime science
33. Conclusion 1: More digital crime than expected Prevalence: most digital crime: Fraud 41% Threats16% More often digital traces Fraud: 29% Commercial burglary: 29% Threats: 18% Residential burglary: 13% Cyber-crime science
34. Conclusion 2: Digital crimes are – partly - different In contrast with [Dom09] findings show – some -departure form traditional offenders Age and sex: no sign differences but trends: towards ‘normalization’ for digital crime In contrast with [UNO13] no indication that ‘digital’ means ‘organised crime’. Instead ‘normalization’ of offenders digital crime more often single offender (fraud), less often a criminal record (threats), and more often legal paid job (threats). ICT brings the modus operandi of crime into the homes Cyber-crime science
35. Limitations Generalisation across crime types is a bad idea Extrapolation of results to other areas of the country probably not a good idea Lower crime rate in smaller urban areas Lower internet use in rural areas Cyber-crime science