Mark Ryan Talabis is the Chief Threat Scientist of Zvelo Inc. Previously he was the Director of the Cloud Business Unit of FireEye Inc. He was also the Lead Researcher and VP of Secure DNA and was an Information Technology Consultant for the Office of Regional Economic Integration (OREI) of the Asian Development Bank (ADB). ?He is co-author of the book 'Information Security Risk Assessment Toolkit: Practical Assessments through Data Collection and Data Analysis' from Syngress. He has presented in various security and academic conferences and organizations around the world including Blackhat, Defcon, Shakacon, INFORMS, INFRAGARD, ISSA, and ISACA. He has a number of published papers to his name in various peer-reviewed journals and is also an alumni member of the Honeynet Project.He has a Master of Liberal Arts Degree (ALM) in Information Technology from Harvard University and a Master of Science (MS) degree in Information Technology from Ateneo de Manila University. He holds several certifications including a Certified Information Systems Security Professional (CISSP); Certified Information Systems Auditor (CISA); and Certified in Risk and Information Systems Control (CRISC).
Information Security Analytics gives you insights into the practice of analytics and, more importantly, how you can utilize analytic techniques to identify trends and outliers that may not be possible to identify using traditional security analysis techniques. Information Security Analytics dispels the myth that analytics within the information security domain is limited to just security incident and event management systems and basic network analysis. Analytic techniques can help you mine data and identify patterns and relationships in any form of security data. Using the techniques covered in this book, you will be able to gain security insights into unstructured big data of any type. The authors of Information Security Analytics bring a wealth of analytics experience to demonstrate practical, hands-on techniques through case studies and using freely-available tools that will allow you to find anomalies and outliers by combining disparate data sets. They also teach you everything you need to know about threat simulation techniques and how to use analytics as a powerful decision-making tool to assess security control and process requirements within your organization. Ultimately, you will learn how to use these simulation techniques to help predict and profile potential risks to your organization. - Written by security practitioners, for security practitioners- Real-world case studies and scenarios are provided for each analytics technique- Learn about open-source analytics and statistical packages, tools, and applications- Step-by-step guidance on how to use analytics tools and how they map to the techniques and scenarios provided- Learn how to design and utilize simulations for "e;what-if"e; scenarios to simulate security events and processes- Learn how to utilize big data techniques to assist in incident response and intrusion analysis
Front Cover 1
Information Security Analytics: Finding Security Insights, Patterns, and Anomalies in Big Data 4
Copyright 5
Dedication 6
Contents 8
Foreword 12
About the Authors 14
Acknowledgments 16
Chapter 1 - Analytics Defined 18
INTRODUCTION TO SECURITY ANALYTICS 18
CONCEPTS AND TECHNIQUES IN ANALYTICS 19
DATA FOR SECURITY ANALYTICS 21
ANALYTICS IN EVERYDAY LIFE 24
SECURITY ANALYTICS PROCESS 29
REFERENCES 29
Chapter 2 - Primer on Analytical Software and Tools 30
STATISTICAL PROGRAMMING 31
INTRODUCTION TO DATABASES AND BIG DATA TECHNIQUES 32
REFERENCES 39
Chapter 3 - Analytics and Incident Response 40
INTRODUCTION 40
SCENARIOS AND CHALLENGES IN INTRUSIONS AND INCIDENT IDENTIFICATION 41
ANALYSIS OF LOG FILES 42
LOADING THE DATA 44
ANOTHER POTENTIAL ANALYTICAL DATA SET: UNSTACKED STATUS CODES 76
OTHER APPLICABLE SECURITY AREAS AND SCENARIOS 81
SUMMARY 81
FURTHER READING 82
Chapter 4 - Simulations and Security Processes 84
SIMULATION 84
CASE STUDY 86
Chapter 5 - Access Analytics 116
INTRODUCTION 116
TECHNOLOGY PRIMER 117
SCENARIO, ANALYSIS, AND TECHNIQUES 121
CASE STUDY 126
ANALYZING THE RESULTS 134
Chapter 6 - Security and Text Mining 140
SCENARIOS AND CHALLENGES IN SECURITY ANALYTICS WITH TEXT MINING 140
USE OF TEXT MINING TECHNIQUES TO ANALYZE AND FIND PATTERNS IN UNSTRUCTURED DATA 141
STEP BY STEP TEXT MINING EXAMPLE IN R 142
OTHER APPLICABLE SECURITY AREAS AND SCENARIOS 164
Chapter 7 - Security Intelligence and Next Steps 168
OVERVIEW 168
SECURITY INTELLIGENCE 168
SECURITY BREACHES 171
PRACTICAL APPLICATION 172
CONCLUDING REMARKS 177
Index 180
Analytics Defined
Abstract
Knowledge of analytical methods and techniques is essential for uncovering hidden patterns in security-related data. Analytical techniques range from simple descriptive statistics, data visualization methods, and statistical analysis algorithms such as regression, correlation analysis, and support vector machines.
The field of analytics is broad. This chapter will focus on methods particularly useful for discovering security breaches and attacks, and which can be implemented with either free or commonly available software. As there are unlimited ways that an attacker can compromise a system, analysts also need a toolkit of techniques to be creative in analyzing security data. Among tools available for creative analysis, we will examine analytical programming languages allowing an analysts to customize analytical procedures and applications. The concepts introduced in this chapter will provide you with a framework for security analysis, along with useful methods and tools.
Keywords
Big data; CSV; Databases; Distributed file system; Hadoop; Hive; Hive query language; HQL; JSON; Machine learning; MapReduce; Neural networks; Pig; Principal components analysis; Relational database; security analytics; SQL; Statistics; Structured data; Structured query language; Supervised learning; Support vector machines; Text mining; Unstructured data; Unsupervised learning; XML
Introduction to Security Analytics
Concepts and Techniques in Analytics
General Statistics
Machine Learning
Supervised Learning
Linear Regression
Classification Techniques
Unsupervised Learning
Clustering
Principal Components Analysis
Simulations
Text Mining
Erscheint lt. Verlag | 25.11.2014 |
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Sprache | englisch |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Informatik ► Netzwerke ► Sicherheit / Firewall | |
ISBN-10 | 0-12-800506-8 / 0128005068 |
ISBN-13 | 978-0-12-800506-4 / 9780128005064 |
Haben Sie eine Frage zum Produkt? |
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