Data Analytics for Cybersecurity
About this book
As the world becomes increasingly connected, it is also more exposed to a myriad of cyber threats. We need to use multiple types of tools and techniques to learn and understand the evolving threat landscape. Data is a common thread linking various types of devices and end users. Analyzing data across different segments of cybersecurity domains, particularly data generated during cyber-attacks, can help us understand threats better, prevent future cyber-attacks, and provide insights into the evolving cyber threat landscape. This book takes a data oriented approach to studying cyber threats, showing in depth how traditional methods such as anomaly detection can be extended using data analytics and also applies data analytics to non-traditional views of cybersecurity, such as multi domain analysis, time series and spatial data analysis, and human-centered cybersecurity.
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Information
Table of contents
- Cover
- Half-title
- Title page
- Copyright information
- Contents
- Preface
- Acknowledgments
- 1 Introduction: Data Analytics for Cybersecurity
- 2 Understanding Sources of Cybersecurity Data
- 3 Introduction to Data Mining: Clustering, Classification, and Association Rule Mining
- 4 Big Data Analytics and Its Need for Cybersecurity: Advanced DM and Complex Data Types from Cybersecurity Perspective
- 5 Types of Cyberattacks
- 6 Anomaly Detection for Cybersecurity
- 7 Anomaly Detection Methods
- 8 Cybersecurity through Time Series and Spatial Data
- 9 Cybersecurity through Network and Graph Data
- 10 Human-Centered Data Analytics for Cybersecurity
- 11 Future Directions in Data Analytics for Cybersecurity
- References
- Index
