Data Engineering for Cybersecurity

by James Bonifield

Fall 2025, 336 pp

ISBN-13:

9781718504028

Download Chapter 4: Endpoint and Network Data

Look Inside!

Data Engineering for Cybersecurity pages 6-7

Data Engineering for Cybersecurity pages 96-97

Data Engineering for Cybersecurity pages 146-147

When a cybersecurity incident occurs, how does anyone know? Data must leave workstations and servers, bounce across the network, and land in a central database before analysts can use it. This project-based book covers that complex space between an organization’s computers and the security analyst tasked with protecting them. The guidance is straightforward, and the focus is on streamlining the process of gathering, transforming, and storing cybersecurity data using free and open-source tools.

The author elaborates on core logging strategies, while also emphasizing the importance of standardizing data, using encryption to protect transmitted data, and creating “data pipelines” that support cybersecurity, data analytics, and automation needs alike. You’ll how to:

Implement core logging strategies using free tools such as the Elastic stack
Transform data to fit your needs, and configure your tools to send it back and forth
Secure your logging infrastructure by encrypting connections using TLS and SSH
Handle version control and backups with Git, and improve your efficiency with caching

By the end, you’ll know how to enrich your security data, protect it from unauthorized parties, and automate your workflow in the process – leaving more room for creative thinking.

Author Bio

James Bonifield is a cybersecurity professional with over a decade of experience analyzing malicious activity, implementing data pipelines, and training others in the security industry. He enjoys spending free time with his wife and kids, traveling, and tinkering with all things security and Python related.

Table of contents

Acknowledgments
Introduction

Part I: Foundations of Secure Data Engineering
Chapter 1: Data Engineering Basics
Chapter 2: Network Encryption
Chapter 3: Source and Configuration Management

Part II: Log Extraction and Management
Chapter 4: Endpoint and Network Data
Chapter 5: Windows Logs
Chapter 6: Integrating and Storing Data
Chapter 7: Working with Syslog Data

Part III: Data Transformation and Standardization
Chapter 8: Data Manipulation Pipelines
Chapter 9: Transformation Filters

Part IV: Data Centralization, Automation, and Enrichment
Chapter 10: Centralizing Security Data
Chapter 11: Automating Tool Configurations
Chapter 12: Ansible Tasks and Playbooks
Chapter 13: Caching Threat Intelligence Data

Index