Suchergebnisse

PrefaceList of FiguresList of TablesIntroductionCyberspace2.1 What is cyberspace?2.2 The impact of cyberspace2.3 Identity and authentication2.4 Encryption2.5 Crime is changing2.6 Policing is changingNew opportunities for criminality3.1 Unprecedented access to information3.2 Crimes directed against cyberspace3.2.1 Malware3.2.2 Crimes of destruction3.2.3 Monetized cybercrimes3.2.4 Data theft crimes3.2.5 Secondary markets3.3 Crimes that rely on cyberspace3.3.1 Spam, scams, and cons3.3.2 Financial crime3.3.3 Online shopping3.3.4 Crimes against children3.4 Crimes done differently because of cyberspace3.4.1 Disseminating hatred3.4.2 Selling drugs3.4.3 Stalking and crime preparation3.4.4 Digital vigilantes3.5 Money laundering3.5.1 Cash3.5.2 The financial system3.5.3 International money laundering3.5.4 Cryptocurrencies3.6 Overlap with violent extremismNew ways for criminals to interact4.1 Criminal collaboration4.2 Planning together4.3 Information sharing4.3.1 Sharing techniques4.3.2 Sharing resources4.3.3 Sharing vulnerabilities4.4 International interactionsData analytics makes criminals easier to find5.1 Understanding by deduction5.2 Understanding by induction5.3 Subverting data analytics5.4 Intelligence-led policing5.5 Hot spot policing5.5.1 Place5.5.2 Time5.5.3 Weather5.5.4 People involved5.5.5 Social network position5.6 Exploiting skewed distributionsData collection6.1 Ways to collect data6.2 Types of data collected6.2.1 Focused data6.2.2 Large volume data6.2.3 Incident data6.2.4 Spatial data6.2.5 Temporal data6.2.6 Non-crime data6.2.7 Data fusion6.2.8 Protecting data collected by law enforcement6.3 Issues around data collection6.3.1 Suspicion6.3.2 Wholesale data collection6.3.3 Privacy6.3.4 Racism and other -isms6.3.5 Errors6.3.6 Bias6.3.7 Sabotaging data collection6.3.8 Getting better data by sharingTechniques for data analytics7.1 Clustering7.2 Prediction7.3 Meta issues in prediction7.3.1 Classification versus regression7.3.2 Problems with the data7.3.3 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 stages7.3.7 Bagging and boosting7.3.8 Anomaly detection7.3.9 Ranking7.3.10 Should I make a prediction at all?7.4 Prediction techniques7.4.1 Counting techniques7.4.2 Optimization techniques7.4.3 Other ensembles7.5 Social network analysis7.6 Natural language analytics7.7 Making data analytics available7.8 Demonstrating complianceCase studies8.1 Predicting crime rates8.2 Clustering RMS data8.3 Geographical distribution patterns8.4 Risk of gun violence8.5 Copresence networks8.6 Criminal networks with a purpose8.7 Analyzing online posts8.7.1 Detecting abusive language8.7.2 Detecting intent8.7.3 Deception8.7.4 Detecting fraud in text8.7.5 Detecting sellers in dark-web marketplaces8.8 Behavior ⁰́₃ detecting fraud from mouse movements8.9 Understanding drug trafficking pathwaysLaw enforcement can use interaction too9.1 Structured interaction through transnational organizations9.2 Divisions within countries9.3 Sharing of information about crimes9.4 Sharing of data9.5 Sharing models9.6 International issuesSummaryIndex

Discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a range of application areas. This book helps you determine which matrix is appropriate for your dataset and what the results mean. It also shows how matrix decompositions can be used to find documents on the Internet.

International audience ; The contribution of this article is twofold: the adaptation and application of models of deception from psychology, combined with data-mining techniques, to the text of speeches given by candidates in the 2008 U.S. presidential election; and the observation of both short-term andmedium-term differences in the levels of deception. Rather than considering the effect of deception on voters, deception is used as a lens through which to observe the self-perceptions of candidates and campaigns. The method of analysis is fully automated and requires no human coding, and so can be applied to many other domains in a straightforward way. The authors posit explanations for the observed variation in terms of a dynamic tension between the goals of campaigns at each moment in time, for example gaps between their view of the candidate's persona and the persona expected for the position; and the difficulties of crafting and sustaining a persona, for example, the cognitive cost and the need for apparent continuity with past actions and perceptions. The changes in the resulting balance provide a new channel by which to understand the drivers of political campaigning, a channel that is hard to manipulate because its markers are created subconsciously.

International audience The contribution of this article is twofold: the adaptation and application of models of deception from psychology, combined with data-mining techniques, to the text of speeches given by candidates in the 2008 U.S. presidential election; and the observation of both short-term andmedium-term differences in the levels of deception. Rather than considering the effect of deception on voters, deception is used as a lens through which to observe the self-perceptions of candidates and campaigns. The method of analysis is fully automated and requires no human coding, and so can be applied to many other domains in a straightforward way. The authors posit explanations for the observed variation in terms of a dynamic tension between the goals of campaigns at each moment in time, for example gaps between their view of the candidate's persona and the persona expected for the position; and the difficulties of crafting and sustaining a persona, for example, the cognitive cost and the need for apparent continuity with past actions and perceptions. The changes in the resulting balance provide a new channel by which to understand the drivers of political campaigning, a channel that is hard to manipulate because its markers are created subconsciously.

Drawing on a collection of open source data, the article uses network analysis to represent alliances and conflicts among 179 organizations involved in violence in North and Western Africa between 1997 and 2014. Owing to the fundamentally relational nature of internecine violence, this article investigates the way the structural positions of conflicting parties affect their ability to resort to political violence. To this end, we combine two spectral embedding techniques that have previously been considered separately: one for directed graphs that takes into account the direction of relationships between belligerents, and one for signed graphs that takes into consideration whether relationships between groups are positive or negative. We hypothesize that groups with similar allies and foes have similar patterns of aggression. In a region where alliances are fluid and actors often change sides, the propensity to use political violence corresponds to a group's position in the social network.