CyberWar

Si Vis Pacem, Para Bellum

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21 Recipes for Mining Twitter

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Millions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights. This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy-to-learn Python tools. Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit your particular needs. The recipes include techniques to:Use OAuth to access Twitter dataCreate and analyze graphs of retweet relationshipsUse the streaming API to harvest tweets in realtimeHarvest and analyze friends and followersDiscover friendship cliquesSummarize webpages from short URLs

This book is a perfect companion to O’Reilly's Mining the Social Web.

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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More

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Amazon Price: $44.99 $34.75 You save: $10.24 (23%). (as of April 27, 2017 00:15 – Details). Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on the Amazon site at the time of purchase will apply to the purchase of this product.

How can you tap into the wealth of social web data to discover who’s making connections with whom, what they’re talking about, and where they’re located? With this expanded and thoroughly revised edition, you’ll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs.
Employ the Natural Language Toolkit, NetworkX, and other scientific computing tools to mine popular social web sites Apply advanced text-mining techniques, such as clustering and TF-IDF, to extract meaning from human language data Bootstrap interest graphs from GitHub by discovering affinities among people, programming languages, and coding projects Build interactive visualizations with D3.js, an extraordinarily flexible HTML5 and JavaScript toolkit Take advantage of more than two-dozen Twitter recipes, presented in O’Reilly’s popular "problem/solution/discussion" cookbook format
The example code for this unique data science book is maintained in a public GitHub repository. It’s designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.

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Selinux: NSA’s Open Source Security Enhanced Linux

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Amazon Price: $39.95 $28.92 You save: $11.03 (28%). (as of April 27, 2017 09:18 – Details). Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on the Amazon site at the time of purchase will apply to the purchase of this product.

The intensive search for a more secure operating system has often left everyday, production computers far behind their experimental, research cousins. Now SELinux (Security Enhanced Linux) dramatically changes this. This best-known and most respected security-related extension to Linux embodies the key advances of the security field. Better yet, SELinux is available in widespread and popular distributions of the Linux operating system–including for Debian, Fedora, Gentoo, Red Hat Enterprise Linux, and SUSE–all of it free and open source.

SELinux emerged from research by the National Security Agency and implements classic strong-security measures such as role-based access controls, mandatory access controls, and fine-grained transitions and privilege escalation following the principle of least privilege. It compensates for the inevitable buffer overflows and other weaknesses in applications by isolating them and preventing flaws in one application from spreading to others. The scenarios that cause the most cyber-damage these days–when someone gets a toe-hold on a computer through a vulnerability in a local networked application, such as a Web server, and parlays that toe-hold into pervasive control over the computer system–are prevented on a properly administered SELinux system.
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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More

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Amazon Price: N/A (as of April 27, 2017 08:38 – Details). Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on the Amazon site at the time of purchase will apply to the purchase of this product.

How can you tap into the wealth of social web data to discover who’s making connections with whom, what they’re talking about, and where they’re located? With this expanded and thoroughly revised edition, you’ll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs.

Employ the Natural Language Toolkit, NetworkX, and other scientific computing tools to mine popular social web sitesApply advanced text-mining techniques, such as clustering and TF-IDF, to extract meaning from human language dataBootstrap interest graphs from GitHub by discovering affinities among people, programming languages, and coding projectsBuild interactive visualizations with D3.js, an extraordinarily flexible HTML5 and JavaScript toolkitTake advantage of more than two-dozen Twitter recipes, presented in O’Reilly’s popular "problem/solution/discussion" cookbook format
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Learning Spark: Lightning-Fast Big Data Analysis

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Amazon Price: N/A (as of April 27, 2017 00:11 – Details). Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on the Amazon site at the time of purchase will apply to the purchase of this product.

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shellLeverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlibUse one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and StormLearn how to deploy interactive, batch, and streaming applicationsConnect to data sources including HDFS, Hive, JSON, and S3Master advanced topics like data partitioning and shared variables

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