Book contents
- Data Analytics for Cybersecurity
- Data Analytics for Cybersecurity
- Copyright page
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- 2 Understanding Sources of Cybersecurity Data
- 3 Introduction to Data Mining
- 4 Big Data Analytics and Its Need for Cybersecurity
- 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
11 - Future Directions in Data Analytics for Cybersecurity
Published online by Cambridge University Press: 10 August 2022
Book contents
- Data Analytics for Cybersecurity
- Data Analytics for Cybersecurity
- Copyright page
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- 2 Understanding Sources of Cybersecurity Data
- 3 Introduction to Data Mining
- 4 Big Data Analytics and Its Need for Cybersecurity
- 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
Summary
This chapter discusses several key directions such as data analytics in cyberphysical systems, multidomain mining, machine Learning concepts such as deep learning, generative adversarial networks, and challenges of model reuse. Last but not the least, the chapter closes with thoughts on ethical thinking in the data analytics process.
Keywords
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- Information
- Data Analytics for Cybersecurity , pp. 147 - 164Publisher: Cambridge University PressPrint publication year: 2022