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E-raamat: Cyberspace, Data Analytics, and Policing [Taylor & Francis e-raamat]

(Queen's University, Canada)
  • Formaat: 258 pages, 5 Tables, black and white; 24 Line drawings, black and white; 24 Illustrations, black and white
  • Ilmumisaeg: 18-Nov-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003126225
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  • Taylor & Francis e-raamat
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Cyberspace, Data Analytics, and PolicingSuurem pilt
  • Formaat: 258 pages, 5 Tables, black and white; 24 Line drawings, black and white; 24 Illustrations, black and white
  • Ilmumisaeg: 18-Nov-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003126225
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003126225
"Cyberspace is changing the face of crime. For criminals it has become a place for rich collaboration and learning, not just within one country; and a place where new kinds of crimes can be carried out, and a vehicle for committing conventional crimes with unprecedented range, scale, and speed. Law enforcement faces a challenge in keeping up and dealing with this new environment. The news is not all bad - collecting and analyzing data about criminals and their activities can provide new levels of insightinto what they are doing and how they are doing it. However, using data analytics requires a change of process and new skills that (so far) many law enforcement organizations have had difficulty leveraging. Cyberspace, Data Analytics, and Policing surveys the changes that cyberspace has brought to criminality and to policing with enough technical content to expose the issues and suggest ways in which law enforcement organizations can adapt"--

Cyberspace, Data Analytics, and Policing surveys the changes that cyberspace has brought to criminality and to policing with enough technical content to expose the issues and suggest ways in which law enforcement organizations can adapt.



Cyberspace is changing the face of crime. For criminals it has become a place for rich collaboration and learning, not just within one country; and a place where new kinds of crimes can be carried out, and a vehicle for committing conventional crimes with unprecedented range, scale, and speed. Law enforcement faces a challenge in keeping up and dealing with this new environment. The news is not all bad – collecting and analyzing data about criminals and their activities can provide new levels of insight into what they are doing and how they are doing it. However, using data analytics requires a change of process and new skills that (so far) many law enforcement organizations have had difficulty leveraging. Cyberspace, Data Analytics, and Policing surveys the changes that cyberspace has brought to criminality and to policing with enough technical content to expose the issues and suggest ways in which law enforcement organizations can adapt.

Key Features:

  • Provides a non-technical but robust overview of how cyberspace enables new kinds of crime and changes existing crimes.
  • Describes how criminals exploit the ability to communicate globally to learn, form groups, and acquire cybertools.
  • Describes how law enforcement can use the ability to collect data and apply analytics to better protect society and to discover and prosecute criminals.
  • Provides examples from open-source data of how hot spot and intelligence-led policing can benefit law enforcement.
  • Describes how law enforcement can exploit the ability to communicate globally to collaborate in dealing with trans-national crime.
Preface xi
List of Figures
xiii
List of Tables
xv
1 Introduction
1(2)
2 Cyberspace
3(18)
2.1 What is cyberspace?
3(3)
2.2 The impact of cyberspace
6(1)
2.3 Identity and authentication
7(4)
2.4 Encryption
11(4)
2.5 Crime is changing
15(2)
2.6 Policing is changing
17(4)
3 New opportunities for criminality
21(40)
3.1 Unprecedented access to information
21(2)
3.2 Crimes directed against cyberspace
23(13)
3.2.1 Malware
23(4)
3.2.2 Crimes of destruction
27(2)
3.2.3 Monetized cybercrimes
29(2)
3.2.4 Data theft crimes
31(2)
3.2.5 Secondary markets
33(3)
3.3 Crimes that rely on cyberspace
36(6)
3.3.1 Spam, scams, and cons
37(3)
3.3.2 Financial crime
40(1)
3.3.3 Online shopping
40(2)
3.3.4 Crimes against children
42(1)
3.4 Crimes done differently because of cyberspace
42(5)
3.4.1 Disseminating hatred
42(1)
3.4.2 Selling drugs
43(2)
3.4.3 Stalking and crime preparation
45(1)
3.4.4 Digital vigilantes
46(1)
3.5 Money laundering
47(8)
3.5.1 Cash
48(1)
3.5.2 The financial system
49(4)
3.5.3 International money laundering
53(1)
3.5.4 Cryptocurrencies
54(1)
3.6 Overlap with violent extremism
55(6)
4 New ways for criminals to interact
61(8)
4.1 Criminal collaboration
61(2)
4.2 Planning together
63(1)
4.3 Information sharing
64(3)
4.3.1 Sharing techniques
64(2)
4.3.2 Sharing resources
66(1)
4.3.3 Sharing vulnerabilities
66(1)
4.4 International interactions
67(2)
5 Data analytics makes criminals easier to find
69(32)
5.1 Understanding by deduction
70(4)
5.2 Understanding by induction
74(5)
5.3 Subverting data analytics
79(2)
5.4 Intelligence-led policing
81(1)
5.5 Hot spot policing
82(9)
5.5.1 Place
83(2)
5.5.2 Time
85(1)
5.5.3 Weather
86(1)
5.5.4 People involved
87(1)
5.5.5 Social network position
87(4)
5.6 Exploiting skewed distributions
91(10)
6 Data collection
101(28)
6.1 Ways to collect data
101(3)
6.2 Types of data collected
104(10)
6.2.1 Focused data
104(1)
6.2.2 Large volume data
105(4)
6.2.3 Incident data
109(1)
6.2.4 Spatial data
109(1)
6.2.5 Temporal data
110(1)
6.2.6 Non-crime data
111(1)
6.2.7 Data fusion
112(2)
6.2.8 Protecting data collected by law enforcement
114(1)
6.3 Issues around data collection
114(15)
6.3.1 Suspicion
114(2)
6.3.2 Wholesale data collection
116(2)
6.3.3 Privacy
118(1)
6.3.4 Racism and other-isms
118(1)
6.3.5 Errors
119(1)
6.3.6 Bias
120(3)
6.3.7 Sabotaging data collection
123(1)
6.3.8 Getting better data by sharing
124(5)
7 Techniques for data analytics
129(52)
7.1 Clustering
129(8)
7.2 Prediction
137(3)
7.3 Meta issues in prediction
140(14)
7.3.1 Classification versus regression
140(1)
7.3.2 Problems with the data
140(2)
7.3.3 Why did the model make this prediction?
142(1)
7.3.4 How good is this model?
143(3)
7.3.5 Selecting attributes
146(2)
7.3.6 Making predictions in stages
148(1)
7.3.7 Bagging and boosting
149(3)
7.3.8 Anomaly detection
152(1)
7.3.9 Ranking
152(1)
7.3.10 Should I make a prediction at all?
153(1)
7.4 Prediction techniques
154(8)
7.4.1 Counting techniques
154(6)
7.4.2 Optimization techniques
160(2)
7.4.3 Other ensembles
162(1)
7.5 Social network analysis
162(9)
7.6 Natural language analytics
171(5)
7.7 Making data analytics available
176(1)
7.8 Demonstrating compliance
177(4)
8 Case studies
181(48)
8.1 Predicting crime rates
181(3)
8.2 Clustering RMS data
184(9)
8.3 Geographical distribution patterns
193(3)
8.4 Risk of gun violence
196(3)
8.5 Copresence networks
199(5)
8.6 Criminal networks with a purpose
204(2)
8.7 Analyzing online posts
206(14)
8.7.1 Detecting abusive language
209(3)
8.7.2 Detecting intent
212(3)
8.7.3 Deception
215(2)
8.7.4 Detecting fraud in text
217(2)
8.7.5 Detecting sellers in dark-web marketplaces
219(1)
8.8 Behavior -- detecting fraud from mouse movements
220(1)
8.9 Understanding drug trafficking pathways
221(8)
9 Law enforcement can use interaction too
229(10)
9.1 Structured interaction through transnational organizations
230(2)
9.2 Divisions within countries
232(1)
9.3 Sharing of information about crimes
233(1)
9.4 Sharing of data
233(2)
9.5 Sharing models
235(1)
9.6 International issues
236(3)
10 Summary
239(12)
Bibliography 251(4)
Index 255
David B. Skillicorn is a professor at the School of Computing, Queen's University, Canada.
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