Data analytics for cybersecurity

著者

    • Janeja, Vandana P.

書誌事項

Data analytics for cybersecurity

Vandana P Janeja

Cambridge University Press, 2022

大学図書館所蔵 件 / 全1件

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注記

Content Type: text (rdacontent), Media Type: unmediated (rdamedia), Carrier Type: volume (rdacarrier)

Includes bibliographical references and index

Summary: "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"-- Provided by publisher

収録内容

  • Introduction - data analytics for cybersecurity
  • Understanding sources of cybersecurity data
  • Introduction to data mining : clustering, classification and association rule mining
  • Big data analytics and its need for cybersecurity
  • Types of cyber attacks
  • Anomaly detection for cyber security
  • Anomaly detection
  • Cybersecurity through time series and spatial data
  • Cybersecurity through network and graph data
  • Human centered data analytics for cyber security
  • Future directions in data analytics for cybersecurity

内容説明・目次

内容説明

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.

目次

  • Preface
  • 1. Introduction
  • 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 Cyber Attacks
  • 6. Anomaly Detection for cyber security
  • 7. Anomaly Detection
  • 8. Cybersecurity through Time Series and Spatial data
  • 9. Cybersecurity through Network and Graph Data
  • 10. Human Centered Data Analytics for Cyber security
  • 11. Future directions in Data Analytics for Cybersecurity
  • References
  • Index

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