Mining Massive Data Sets for Security ：Advances in Data Mining, Search, Social Networks and Text Mining, and their Applications to Security ( NATO Science for Peace and Security Series - D: Information and Communication Security )

Publication subTitle ：Advances in Data Mining, Search, Social Networks and Text Mining, and their Applications to Security

Publication series ： NATO Science for Peace and Security Series - D: Information and Communication Security

Author： Fogelman-Soulié F.;Perrotta D.;Piskorski J.  

Publisher： Ios Press‎

Publication year： 2008

E-ISBN: 9781607503620

P-ISBN(Paperback): 9781586038984

Subject： TP3 Computers

Keyword： 计算技术、计算机技术

Language： ENG

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Description

The real power for security applications will come from the synergy of academic and commercial research focusing on the specific issue of security. Special constraints apply to this domain, which are not always taken into consideration by academic research, but are critical for successful security applications: large volumes: techniques must be able to handle huge amounts of data and perform ‘on-line’ computation; scalability: algorithms must have processing times that scale well with ever growing volumes; automation: the analysis process must be automated so that information extraction can ‘run on its own’; ease of use: everyday citizens should be able to extract and assess the necessary information; and robustness: systems must be able to cope with data of poor quality (missing or erroneous data). The NATO Advanced Study Institute (ASI) on Mining Massive Data Sets for Security, held in Italy, September 2007, brought together around ninety participants to discuss these issues. This publication includes the most important contributions, but can of course not entirely reflect the lively interactions which allowed the participants to exchange their views and share their experience. The bridge between academic methods and industrial constraints is systematically discussed throughout. This volume will thus serve as a reference book for anyone interested in understanding the techniques for handling very large data sets and how to apply them in conjunction for solving security iss

Chapter

Title page

Mining Massive Data Sets for Security

Contents

Data Mining

Learning Using Hidden Information: Master-Class Learning

Learning Using Large Datasets

Practical Feature Selection: From Correlation to Causality

Industrial Mining of Massive Data Sets

Large-Scale Semi-Supervised Learning

User Modeling and Machine Learning: A Survey

Smoothness and Sparsity Tuning for Semi-Supervised SVM

Distributed Categorizer for Large Category Systems

Data Stream Management and Mining

Modelling and Analysing Systems of Agents by Agent-Aware Transition Systems

Search

The "Real World" Web Search Problem: Bridging the Gap Between Academic and Commercial Understanding of Issues and Methods

Website Privacy Preservation for Query Log Publishing

Fighting Web Spam

Social Networks

Emergent Patterns in Online Coactivity

Diffusion and Cascading Behavior in Networks

Link Analysis in Networks of Entities

Evolving Networks

Mining Networks Through Visual Analytics: Incremental Hypothesis Building and Validation

A Review of Anomaly Detection on Graphs

Text Mining

Using Language-Independent Rules to Achieve High Multilinguality in Text Mining

Mining the Web to Build a Complete, Large-Scale Language Model

Integrating Text Mining and Link Analysis

Using Linguistic Information as Features for Text Categorization

Security Applications

Statistical Techniques for Fraud Detection, Prevention and Assessment

Fitting Mixtures of Regression Lines with the Forward Search

Money Laundering Detection Using Data Mining

Text Mining from the Web for Medical Intelligence

Learning to Populate an Ontology of Politically Motivated Violent Events

Filtering Multilingual Terrorist Content with Graph-Theoretic Classification Tools

Open Source Intelligence

Detecting Core Members in Terrorist Networks: A Case Study

Geolocalisation in Cellular Telephone Networks

Machine Learning for Intrusion Detection

Subject Index

Author Index

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