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Presentation to 2014 American Society of Criminology conference on a study of life-course cybercriminology carried out with samples in the United States and India.
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Life-course Cybercriminology:
US and Indian samples
Mark Stockman, Hanif Qureshi, William
Mackey, Michael Holiday, University of
Cincinnati
Thomas J. Holt, Michigan State University
Criminological Ubiquity
• Age-crime curve
• Gender
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
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%
Methods
• Exploratory retrospective study
• Undergraduate students
• Paper surveys
• 25 cyberdeviant behaviors
• US (2013) and Indian Samples (2014)
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
US
• n = 269
• Mean/median age = 20.95/20 (18-41)
• Male/Female = 208/52 (77.3% male)
India
• n = 354
• Mean/median age = 23.08/22 (18-47)
• Male/female = 253/108 (69.9% male)
Age of Onset (US)
Average Peak Age (US)
Cybercrime Curve (US)
• 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
Hacking Prevalence (US)
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 proportions significant (p < .001)
Hacking Prevalence (US)
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 proportions significant (p < .001)
Hacking Prevalence (India)
Major Gender Percent N
Non-Computing
male 75.0%** 176
female 47.4%** 78
Total 66.5% 254
Computing
male 83.6%** 61
female 60.0%** 25
Total 76.7% 86
Total
male 77.3%** 238
female 54.5%** 112
Total 69.1% 340
** Difference in proportions significant (p < .001)
Hacking Prevalence (India)
Major Gender Percent N
Non-Computing
male 75.0%** 176
female 47.4%** 78
Total 66.5% 254
Computing
male 83.6%** 61
female 60.0%** 25
Total 76.7% 86
Total
male 77.3%** 238
female 54.5%** 112
Total 69.1% 340
** Difference in proportions significant (p < .001)
Hacking Prevalence
US Sample Indian Sample
Major Gender Percent n Percent n
Non-Computing
male 55.7% 61 75.0%** 176
female 62.5% 32 47.4%** 78
Total 58.1%** 93 66.5% 254
Computing
male 81.8% 137 83.6%** 61
female 66.7% 18 60.0%** 25
Total 80.0%** 155 76.7% 86
Total
male 73.7% 198 77.3%** 238
female 64.0% 50 54.5%** 112
Total 71.8% 248 69.1% 340
** Difference in proportions significant (p < .001)
Hacking Prevalence
US Sample Indian Sample
Major Gender Percent n Percent n
Non-Computing
male 55.7% 61 75.0%** 176
female 62.5% 32 47.4%** 78
Total 58.1%** 93 66.5* 254
Computing
male 81.8% 137 83.6%** 61
female 66.7% 18 60.0%** 25
Total 80.0%** 155 76.7* 86
Total
male 73.7% 198 77.3%** 238
female 64.0% 50 54.5%** 112
Total 71.8% 248 69.1% 340
** Difference in proportions significant (p < .001)
Hacking Behaviors (US)
* 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 Behaviors (India)
* 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 4.38** 176 4.045
female 2.23** 78 3.438
Total 3.72 254 4.315
Computing
male 5.33 61 4.352
female 3.12 25 4.150
Total 4.67 86 4.387
Total
male 4.63** 238 4.476
female 2.76** 112 3.889
Total 3.96 340 4.347
Hacking Behaviors (India)
* Difference in means between male/female students significant (p < .01) ** Difference in means between computing/non-computing majors significant (p < .001)
US Sample Indian Sample
Major Gender Mean N Sdev Mean N Sdev
Non-Computing
male 1.69 61 2.997 4.38** 176 4.045
female 1.31 32 1.424 2.23** 78 3.438
Total 1.56** 93 2.564 3.72 254 4.315
Computing
male 3.64 137 3.823 5.33 61 4.352
female 2 18 2.401 3.12 25 4.15
Total 3.45** 155 3.718 4.67 86 4.387
Total
male 3.04* 198 3.694 4.63** 238 4.476
female 1.56* 50 1.842 2.76** 112 3.889
Total 2.74 248 3.451 3.96 340 4.347
India Comparison
• Gender – Greater differences
• Major – Lesser differences
• Behaviors – Significantly more across the
board (p < .001)
Why not? (US)
“I’ve never had the need, skillset, or knowledge”
“Been too busy to learn”
“Number 1 it is wrong. Number 2 I would have no idea where to start”
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Life-course Cybercriminology
Mark Stockman
Thomas J. Holt
William Mackey
Hanif Qureshi
Michael Holiday
