Özyer / Erdem / Rokne Mining Social Networks and Security Informatics

1. Auflage 2013
ISBN: 978-94-007-6359-3
Verlag: Springer Netherland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 283 Seiten

Reihe: Computer Science (R0)

Crime, terrorism and security are in the forefront of current societal concerns. This edited volume presents research based on social network techniques showing how data from crime and terror networks can be analyzed and how information can be extracted. The topics covered include crime data mining and visualization; organized crime detection; crime network visualization; computational criminology; aspects of terror network analyses and threat prediction including cyberterrorism and the related area of dark web; privacy issues in social networks; security informatics; graph algorithms for social networks; general aspects of social networks such as pattern and anomaly detection; community discovery; link analysis and spatio-temporal network mining. These topics will be of interest to researchers and practitioners in the general area of security informatics.  The volume will also serve as a general reference for readers that would want to become familiar with current research in the fastgrowing field of cybersecurity.

Zielgruppe

Research

Autoren/Hrsg.

Özyer, Tansel

Erdem, Zeki

Rokne, Jon

Khoury, Suheil

Inhaltsverzeichnis

A Model for Dynamic Integration of Data Sources.- Overlapping community structure and modular overlaps in complex networks.- Constructing and Analyzing Uncertain Social Networks from Unstructured Textual Data.- Privacy Breach Analysis in Social Networks.- Partitioning Breaks Communities.- SAINT: Supervised Actor Identification for Network Tuning.- Holder and Topic based Analysis of Emotions on Blog Texts: a Case Study for Bengali.- Predicting Number of Zombies in a DDoS Attacks using Isotonic Regression.- Developing a Hybrid Framework for a Web-Page Recommender System.- Evaluation and Development of Data Mining Tools for Social Network Analysis.- Learning To Detect Vandalism in Social Content Systems: A Study On Wikipedia Vandalism Detection in Wikipedia.- Perspective on Measurement Metrics for Community Detection Algorithms.- A Study of Malware Propagation via Online Social Networking.- Estimating the Importance of Terrorists in a Terror Network.