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Life-course cybercriminology study on hacking activities.TRANSCRIPT
Hacking the age-crime curve:
A study in life-course
Cybercriminology
Mark Stockman, University of Cincinnati
Thomas J. Holt, Michigan State University
William Mackey, University of Cincinnati
Michael Holiday, University of Cincinnati
Criminological Ubiquity
• Age-crime curve
• Opportunity
• Gender
Age-crime Curve
Farrington, D. P. (1986). Age and crime. Crime and justice, 189-250.
Onset Rate
Wolfgang, M. E., Thornberry, T. P., & Figlio, R. M. (1987). From boy to man, from delinquency to crime. University of Chicago Press.
Onset Means
Le Blanc, M., & Fréchette, M. (1989). Male Criminal Activity from Childhood through Youth: Multi—Level and Developmental Perspectives.
2012 US Arrest Data by Gender
Uniform Crime Report, Crime in the United States, 2012
Offense Charged Male Female
Violent crime 80.1% 19.9%
Property crime 62.6% 37.4%
All Offenses 73.8% 26.2%
Forgery and counterfeiting 62.8% 37.2%
Fraud 59.5% 40.5%
Embezzlement 51.6% 48.4%
Stolen property; buying,
receiving, possessing79.7% 20.3%
Vandalism 79.8% 20.2%
Cybercrime
• H1: Earlier onset and peak
– “Point and shoot” hacking tools
– Online tutorials
– Limited parental social control
• H2: Male dominant participation
• H3: Computing ability/interest matters
Methods
• Exploratory retrospective study
– Lack of official data
– Victimization often undetectable
• Undergraduate students
• Oversample computing majors
• Paper surveys in-class
• Onset and peak age of 25 cyberdeviant
behaviors
Behavior Examples
Guessed passwords to access wireless
networks
Altered another’s wireless router settings
Attempted SQL injections against websites
Used Metasploit to exploit another’s computer
Used a bot to perform DOS or other attack
Knowingly sent out phishing e-mails
Used a man in the middle attack to direct users
to altered sites
Sample Descriptives
• n = 269 (5 removed, underage)
• Mean/median age = 20.95/20 (18-41)
• Male/Female = 208/52 (77.3% male)
• Computing majors = 167 (62.1%)
• Arrested for cybercrime = 1 (twice)
Age of Onset
Average Peak Age
Cybercrime Curve
• Similar pattern to typical crime
• Early onset and peak not detectable
• Age of sample
– Mean age of 20.95
– Online tutorials and “point and shoot tools”
more recent development
• H1 may bear out for kids today
Opportunity/Gender
Major Gender Percent N
Non-Computing
male 55.7% 61
female 62.5% 32
Total 58.1%** 93
Computing
male 81.8% 137
female 66.7% 18
Total 80.0%** 155
Total
male 73.7% 198
female 64.0% 50
Total 71.8% 248
** Difference in means between computing/non-computing majors significant (p < .001)
Hacking Incidence
Opportunity/Gender
Major Gender Percent N
Non-Computing
male 55.7% 61
female 62.5% 32
Total 58.1%** 93
Computing
male 81.8% 137
female 66.7% 18
Total 80.0%** 155
Total
male 73.7% 198
female 64.0% 50
Total 71.8% 248
** Difference in means between computing/non-computing majors significant (p < .001)
Hacking Incidence
Opportunity/Gender
* Difference in means between male/female students significant (p < .01) ** Difference in means between computing/non-computing majors significant (p < .001)
Major Gender Mean NStd.
Deviation
Non-Computing
male 1.69 61 2.997
female 1.31 32 1.424
Total 1.56** 93 2.564
Computing
male 3.64 137 3.823
female 2.00 18 2.401
Total 3.45** 155 3.718
Total
male 3.04* 198 3.694
female 1.56* 50 1.842
Total 2.74 248 3.451
Hacking Prevalence
Limitations/Next Steps
• Limitations
– Age of respondents (mean = 20.95)
– Single university/limited breadth of majors
– Participation refusal/response validity
• Next Steps
– Replicate study
– Weight hacking behaviors
– Investigate data further
(control, opportunity, differential
association, motivations, female)
Life-course Cybercriminology
Mark Stockman
William Mackey
Thomas J. Holt
Michael Holiday