Data Analytics for Cybersecurity

Shows how traditional and nontraditional methods such as anomaly detection and time series can be extended using data analytics.

Vandana P. Janeja (Author)

9781108415279, Cambridge University Press

Hardback, published 21 July 2022

240 pages
23.5 x 15.7 x 1.9 cm, 0.45 kg

'Dr. Janeja shows us how data analytics can be used to predict, identify and intercept threats, solving difficult cybersecurity problems in the process. As a Professor and cybersecurity professional, I am thrilled for the prospect of teaching this material to my students and applying it to the industry at large. Simply put, this is the cybersecurity book I've been looking for.' Faisal Quader, Technuf LLC

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.

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

Subject Areas: Computer security [UR], Data mining [UNF]