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

Chapter 7. Fuzzy Hashing

In modern computer forensics, we are tasked with examining massive datasets for evidence that supports or refutes an event. It is quite common to see a case that involves multiple devices or large amounts of data. With the sheer volume of data to evaluate, an examiner must sift out the information that is not relevant to the case and identify the data that is of interest. This process of identification takes a fair amount of time, even with current tools. In this chapter, we are going to explore Python solutions that can help us identify known files in a folder, or a mounted evidence container, in an automated manner.

Commonly, a white or black list can help us identify known files on a system through a matching hash value. If the hash value is a match, we can identify files as normal, malicious, or otherwise notable. But what if a file is not an exact match? This is an issue with the traditional cryptographic hashes we use in forensics to generate a unique hash based...