Book Image

Learning Python for Forensics

By : Chapin Bryce
Book Image

Learning Python for Forensics

By: Chapin Bryce

Overview of this book

This book will illustrate how and why you should learn Python to strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. The tutorials use an interactive design, giving you experience of the development process so you gain a better understanding of what it means to be a forensic developer. Each chapter walks you through a forensic artifact and one or more methods to analyze the evidence. It also provides reasons why one method may be advantageous over another. We cover common digital forensics and incident response scenarios, with scripts that can be used to tackle case work in the field. Using built-in and community-sourced libraries, you will improve your problem solving skills with the addition of the Python scripting language. In addition, we provide resources for further exploration of each script so you can understand what further purposes Python can serve. With this knowledge, you can rapidly develop and deploy solutions to identify critical information and fine-tune your skill set as an examiner.
Table of Contents (24 chapters)
Learning Python for Forensics
Credits
About the Authors
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
Index

Summary


In this chapter, you learned how to parse a plain text file using Python. This process can be implemented for other log files including those from firewalls, web servers, or other applications and services. Following these steps, we can identify repetitive data structures that lend themselves to scripts, process their data, and output results to the user. With our iterative build process, we implemented a test-then-code approach where we built a working prototype and then continually enhanced it into a viable and reliable forensic tool.

In addition to the text format we explored here, some files have a more concrete structure and are stored in a serialized format. Other files, such as HTML, XML, and JSON, file structure data in a manner that can be readily converted to a series of Python objects. In the next chapter, we will explore methods in Python to parse, manipulate, and interact with these structured formats.