Title page
Contents
About the editor 6
List of contributors 7
Introduction 12
A. Indigenous over-representation in the criminal justice system 16
1. Intergenerational incarceration in New South Wales: Characteristics of people in prison experiencing parental imprisonment 17
2. Pre-sentence reports for Aboriginal and Torres Strait Islander people: An analysis of language and sentiment 37
B. Transnational serious and organised crime 46
3. Enablers of illicit drug trafficking by organised crime groups 47
4. Predicting high-harm offending using machine learning: An application to outlaw motorcycle gangs 63
5. Regulatory approaches to preventing organised crime among outlaw motorcycle gangs 77
6. Outlaw motorcycle gangs and domestic violence 90
C. Domestic and family violence 104
7. Spaceless violence: Women's experiences of technology‑facilitated domestic violence in regional, rural and remote areas 105
8. Giving voice to the silenced victims: A qualitative study of intimate partner femicide 116
9. The role of depression in intimate partner homicide perpetrated by men against women: An analysis of sentencing remarks 126
D. Sexual violence 140
10. Reporting of dating app facilitated sexual violence to the police: Victim‑survivor experiences and outcomes 141
11. Image‑based abuse: Gender differences in bystander experiences and responses 157
E. Online sexual exploitation of children 172
12. Secrecy, control and violence in women's intimate relationships with child sexual abuse material offenders 173
13. Child sexual abuse material and end‑to‑end encryption on social media platforms: An overview 188
14. The sexual exploitation of Australian children on dating apps and websites 204
15. Advancing child sexual abuse investigations using biometrics and social network analysis 215
16. How to implement online warnings to prevent the use of child sexual abuse material 229
17. The overlap between child sexual abuse live streaming, contact abuse and other forms of child exploitation 241
F. Cybercrime 256
18. Data breaches and cybercrime victimisation 257
19. Help‑seeking among Australian ransomware victims 271
20. Online behaviour, life stressors and profit‑motivated cybercrime victimisation 283
Index 299
1. Intergenerational incarceration in New South Wales: Characteristics of people in prison experiencing parental imprisonment 22
Table 1. History of parental imprisonment reported by adults in prison and young people in youth justice centres in NSW in 2015 22
Table 2. Adults in prison in NSW in 2015 with a history of parental incarceration, by sex of prisoner 22
2. Pre-sentence reports for Aboriginal and Torres Strait Islander people: An analysis of language and sentiment 39
Table 1. Number of pre-sentence reports by type, court and gender 39
Table 2. Mean frequencies and percentage of keywords by category and court type 41
Table 3. Average proportion of pre-sentence reports' text classified as positive or negative 42
3. Enablers of illicit drug trafficking by organised crime groups 52
Table 1. Illicit drug trafficking by organised crime groups, by drug type and role in supply chain 52
Table 2. Characteristics of organised crime groups involved in illicit drug trafficking 54
Table 3. Logistic regression model predicting likelihood of poly-drug trafficking among illicit drug trafficking groups 55
4. Predicting high-harm offending using machine learning: An application to outlaw motorcycle gangs 71
Table 1. Confusion matrix for predicting high-harm offences among OMCG members 71
Table 2. Confusion matrix for predicting any offence among OMCG members 71
5. Regulatory approaches to preventing organised crime among outlaw motorcycle gangs 84
Table 1. ARIMA models examining changes in the organised crime related harm of OMCG members following the introduction of Queensland's occupational restrictions 84
Table 2. Sensitivity analyses with ARIMA models examining artificial occupational restriction covariates at different points of onset prior to actual implementation in July 2014 85
6. Outlaw motorcycle gangs and domestic violence 96
Table 1. Sample characteristics, by OMCG membership 96
Table 2. Characteristics of violent offending histories, by OMCG membership 98
Table 3. Outcomes of finalised matters for violent offences, by OMCG membership 98
8. Giving voice to the silenced victims: A qualitative study of intimate partner femicide 118
Table 1. Characteristics of victims and informants 118
10. Reporting of dating app facilitated sexual violence to the police: Victim‑survivor experiences and outcomes 144
Table 1. Online and in‑person sexual harassment, aggression and violence behaviours 144
Table 2. Prevalence of reporting online and in‑person DAFSV to the police, by respondent gender and sexual identity 147
Table 3. Victim‑survivor satisfaction with police investigation processes, by respondent gender and sexual identity and type of DAFSV 150
11. Image‑based abuse: Gender differences in bystander experiences and responses 161
Table 1. Experiences witnessing IBA 161
Table 2. Actions taken 162
Table 3. Concerns about taking action 163
Table 4. Reasons for taking action 164
Table 5. Reasons for not taking action 165
12. Secrecy, control and violence in women's intimate relationships with child sexual abuse material offenders 177
Table 1. Behaviours of CSAM offenders, as described by their female ex‑partners 177
13. Child sexual abuse material and end‑to‑end encryption on social media platforms: An overview 189
Table 1. Acronyms and terminology 189
Table 2. CSAM reports to NCMEC in 2020, detection methods and end‑to‑end encryption status for top 10 reporting ESPs 192
14. The sexual exploitation of Australian children on dating apps and websites 207
Table 1. Sociodemographic characteristics of survey respondents 207
Table 2. Requests for sexual exploitation of children by people met on dating apps/websites, by respondent gender identity and type of request 208
Table 3. Requests for child sexual exploitation by people met on dating apps/websites, by respondent gender and sexual identity, and type of request 209
17. The overlap between child sexual abuse live streaming, contact abuse and other forms of child exploitation 244
Table 1. Key terminology used in the current study 244
Table 2. Victims and facilitators approached about travelling to offend 245
Table 3. Case study of a 49‑year‑old offender discussing travelling to offend against a 16‑year‑ old victim (Case 2) 246
Table 4. Case study of a 58‑year‑old offender discussing travelling to offend after viewing CSA live streaming (Case 7) 247
Table 5. Material found on offenders' devices 248
Table 6. Victims and facilitators who received requests for or offered CSAM 248
Table 7. Case study of 17‑year‑old CSA live streaming victim offering CSAM to a 42‑year‑old offender (Case 1) 249
18. Data breaches and cybercrime victimisation 261
Table 1. Survey respondents who had been notified that their information had been exposed in a data breach, by selected demographic characteristics and... 261
Table 2. Indicators of identity crime experienced by respondents whose information was exposed in a data breach 263
Table 3. Indicators of identity crime experienced by respondents whose information was exposed in a data breach 265
Table 3. Indicators of identity crime experienced by respondents whose information was exposed in a data breach 266
20. Online behaviour, life stressors and profit‑motivated cybercrime victimisation 288
Table 1. Sample characteristics 288
Table 2. Logistic regression model predicting profit‑motivated cybercrime victimisation in the 12 months prior to the survey 290
1. Intergenerational incarceration in New South Wales: Characteristics of people in prison experiencing parental imprisonment 23
Figure 1. Intergenerational incarceration by age of adult in prison 23
Figure 2. Intergenerational incarceration by highest year of schooling completed 24
Figure 3. Intergenerational incarceration for adults in prison previously incarcerated in youth detention, by age at first incarceration 24
Figure 4. Intergenerational incarceration by care type received by participants before the age of 16 years 26
3. Enablers of illicit drug trafficking by organised crime groups 51
Figure 1. Illicit drug types trafficked by organised crime groups 51
Figure 2. Role of organised crime groups in illicit drug supply chain, all drug types 52
Figure 3. Number of members in organised crime groups involved in illicit drug trafficking 53
Figure 4. Predicted probability of poly-drug trafficking, by presence of enabling activities 57
4. Predicting high-harm offending using machine learning: An application to outlaw motorcycle gangs 68
Figure 1. Number of high-harm offences committed by OMCG members in the five-year reference period 68
Figure 2. ROC curve for random forest model trained on high-harm offending by OMCG members 68
Figure 3. Variable importance associated with high-harm offending among OMCG members 69
Figure 4. Risk of high-harm offence by aggregated weighted harm of prior offences 70
Figure 5. ROC curve for random forest model trained on any offending by OMCG members 71
Figure 6. Variable importance associated with any offending by OMCG members 72
5. Regulatory approaches to preventing organised crime among outlaw motorcycle gangs 82
Figure 1. Monthly organised crime related harm, as measured by the WACHI, by outlaw motorcycle gang members in Queensland, 2011-2016 82
Figure 2. Illustration of post-intervention trend and level reductions in an outcome measure 82
Figure 3. Illustration of different operationalisations of the criminal justice elements of Queensland's 2013 suite of outlaw motorcycle gang measures 83
6. Outlaw motorcycle gangs and domestic violence 94
Figure 1. Prevalence of domestic violence offending, violent non-domestic offending and non‑violent offending in the previous 10 years among OMCG members... 94
Figure 2. Prevalence of domestic violence and violent non-domestic offending among OMCG members in NSW, by club 95
Figure 3. Prevalence of domestic violence offending, violent non-domestic offending and non‑violent offending, by OMCG membership 97
Figure 4. Comparison of predicted guilty outcomes for finalised domestic violence matters among OMCG members and non-OMCG offenders (95% confidence intervals) 99
10. Reporting of dating app facilitated sexual violence to the police: Victim‑survivor experiences and outcomes 146
Figure 1. Reporting of dating app facilitated sexual violence, by type of DAFSV and reporting entity 146
Figure 2. Reports of DAFSV investigated by police, by respondent gender and sexual identity and DAFSV type 148
Figure 3. Victim‑survivor satisfaction with police investigation processes, by type of DAFSV 149
Figure 4. Victim‑survivor level of agreement with the statement 'The police took the incident seriously', by respondent gender and sexual identity and DAFSV type 151
Figure 5. Victim‑survivor level of agreement with the statement 'The police provided me with information about other services that could assist me', by respondent... 152
Figure 6. Victim‑survivor level of agreement with the statement 'I would report to the police again', by respondent gender and sexual identity and DAFSV type 153
13. Child sexual abuse material and end‑to‑end encryption on social media platforms: An overview 190
Figure 1. Flow diagram of resource selection process 190
Figure 2. Number of CSAM reports to NCMEC and Internet Watch Foundation, 2014-2021 191
Figure 3. Explanation of Apple's NeuralHash technology 195
14. The sexual exploitation of Australian children on dating apps and websites 210
Figure 1. Relationship of child target to respondent who received requests for child sexual exploitation by someone on a dating app/website 210
15. Advancing child sexual abuse investigations using biometrics and social network analysis 218
Figure 1. Network of videos extracted from a single investigation 218
Figure 2. Identifying communities of victims/offenders within the primary cluster 220
Figure 3. Degree centrality across the primary cluster 221
Figure 4. Biometric match brokerage configurations 222
Figure 5. Betweenness centrality across the primary cluster 223
16. How to implement online warnings to prevent the use of child sexual abuse material 235
Figure 1. An illustration of the concept of defence in depth 235
18. Data breaches and cybercrime victimisation 264
Figure 1. Estimated probability of identity crime victimisation, by whether respondent was notified of a data breach 264
Figure 2. Estimated probability of online scam or fraud victimisation, by whether respondent was notified their information was exposed in a data breach 267
Figure 3. Estimated probability of ransomware victimisation, by whether respondent was notified their information was exposed in a data breach 267
19. Help‑seeking among Australian ransomware victims 273
Figure 1. People and organisations reported to by ransomware and other malware victims 273
Figure 2. Help‑seeking behaviour among ransomware victims, by age 275
Figure 3. Help‑seeking behaviour among ransomware victims, by SME status 276
Figure 4. Number of different sources of help, advice or support among ransomware victims 276
Figure 5. Satisfaction with the quality of help, advice or support received by ransomware victims by source of help 277
Figure 6. Reasons for reporting incidents to the police or ACSC among ransomware victims 278
Figure 7. Outcome of ransomware incidents reported to the police or ACSC 278
Figure 8. Satisfaction with reporting to the police or ACSC among ransomware victims 279
Figure 9. Reasons for not reporting incidents to the police or ACSC among ransomware victims 280
20. Online behaviour, life stressors and profit‑motivated cybercrime victimisation 292
Figure 1. Predicted probability of identity crime and misuse by SME status and WFH due to COVID‑19 social distancing measures 292
Figure 2. Predicted probability of malware attacks by SME status and WFH due to COVID‑19 social distancing measures 293
Figure 3. Predicted probability of fraud and scams by SME status and WFH due to COVID‑19 social distancing measures 293
1. Intergenerational incarceration in New South Wales: Characteristics of people in prison experiencing parental imprisonment 36
Table A1. Weighting factors used to calculate findings based on NPHS 2015 data 36
15. Advancing child sexual abuse investigations using biometrics and social network analysis 226
Table A1. Full centrality results 226
18. Data breaches and cybercrime victimisation 270
Table A1. Rare events logistic regression estimating likelihood of being a victim of different types of cybercrime 270