Cyberspace, Data Analytics, and Policing

David B. Skillicorn is a professor at the School of Computing, Queen's University, Canada.

• Preface List of Figures List of Tables Introduction Cyberspace2.1 What is cyberspace? 2.2 The impact of cyberspace 2.3 Identity and authentication 2.4 Encryption 2.5 Crime is changing2.6 Policing is changing New opportunities for criminality 3.1 Unprecedented access to information 3.2 Crimes directed against cyberspace 3.2.1 Malware 3.2.2 Crimes of destruction 3.2.3 Monetized cybercrimes 3.2.4 Data theft crimes 3.2.5 Secondary markets 3.3 Crimes that rely on cyberspace 3.3.1 Spam, scams, and cons 3.3.2 Financial crime 3.3.3 Online shopping 3.3.4 Crimes against children 3.4 Crimes done differently because of cyberspace 3.4.1 Disseminating hatred 3.4.2 Selling drugs 3.4.3 Stalking and crime preparation 3.4.4 Digital vigilantes 3.5 Money laundering3.5.1 Cash 3.5.2 The financial system 3.5.3 International money laundering 3.5.4 Cryptocurrencies 3.6 Overlap with violent extremism New ways for criminals to interact 4.1 Criminal collaboration 4.2 Planning together 4.3 Information sharing 4.3.1 Sharing techniques4.3.2 Sharing resources 4.3.3 Sharing vulnerabilities 4.4 International interactionsData analytics makes criminals easier to find5.1 Understanding by deduction 5.2 Understanding by induction 5.3 Subverting data analytics5.4 Intelligence-led policing 5.5 Hot spot policing 5.5.1 Place 5.5.2 Time 5.5.3 Weather 5.5.4 People involved 5.5.5 Social network position 5.6 Exploiting skewed distributions Data collection6.1 Ways to collect data 6.2 Types of data collected 6.2.1 Focused data 6.2.2 Large volume data 6.2.3 Incident data 6.2.4 Spatial data 6.2.5 Temporal data 6.2.6 Non-crime data 6.2.7 Data fusion 6.2.8 Protecting data collected by law enforcement6.3 Issues around data collection 6.3.1 Suspicion 6.3.2 Wholesale data collection 6.3.3 Privacy 6.3.4 Racism and other -isms 6.3.5 Errors 6.3.6 Bias 6.3.7 Sabotaging data collection 6.3.8 Getting better data by sharingTechniques for data analytics 7.1 Clustering 7.2 Prediction 7.3 Meta issues in prediction 7.3.1 Classification versus regression 7.3.2 Problems with the data 7.3.3 Why did the model make this prediction? 7.3.4 How good is this model?7.3.5 Selecting attributes7.3.6 Making predictions in stages 7.3.7 Bagging and boosting 7.3.8 Anomaly detection7.3.9 Ranking7.3.10 Should I make a prediction at all? 7.4 Prediction techniques 7.4.1 Counting techniques 7.4.2 Optimization techniques 7.4.3 Other ensembles7.5 Social network analysis 7.6 Natural language analytics 7.7 Making data analytics available 7.8 Demonstrating compliance Case studies8.1 Predicting crime rates 8.2 Clustering RMS data 8.3 Geographical distribution patterns 8.4 Risk of gun violence 8.5 Copresence networks 8.6 Criminal networks with a purpose 8.7 Analyzing online posts 8.7.1 Detecting abusive language 8.7.2 Detecting intent 8.7.3 Deception 8.7.4 Detecting fraud in text 8.7.5 Detecting sellers in dark-web marketplaces 8.8 Behavior – detecting fraud from mouse movements 8.9 Understanding drug trafficking pathways Law enforcement can use interaction too9.1 Structured interaction through transnational organizations 9.2 Divisions within countries 9.3 Sharing of information about crimes 9.4 Sharing of data 9.5 Sharing models 9.6 International issues SummaryIndex

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