Book Image

Learning Python for Forensics - Second Edition

By : Preston Miller, Chapin Bryce
Book Image

Learning Python for Forensics - Second Edition

By: Preston Miller, Chapin Bryce

Overview of this book

Digital forensics plays an integral role in solving complex cybercrimes and helping organizations make sense of cybersecurity incidents. This second edition of Learning Python for Forensics illustrates how Python can be used to support these digital investigations and permits the examiner to automate the parsing of forensic artifacts to spend more time examining actionable data. The second edition of Learning Python for Forensics will illustrate how to develop Python scripts using an iterative design. Further, it demonstrates how to leverage the various built-in and community-sourced forensics scripts and libraries available for Python today. This book will help strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. By the end of this book, you will build a collection of Python scripts capable of investigating an array of forensic artifacts and master the skills of extracting metadata and parsing complex data structures into actionable reports. Most importantly, you will have developed a foundation upon which to build as you continue to learn Python and enhance your efficacy as an investigator.
Table of Contents (15 chapters)

Fuzzy Hashing

Hashing is one of the most common processes run in DFIR. This process allows us to summarize file content and assign a representative and repeatable signature that represents the file's content. We generally employ file and content hashes using algorithms such as MD5, SHA1, and SHA256. These hash algorithms are valuable as we can use them for integrity validation since a change to even one byte of a file's content will completely alter the resulting hash value. These hashes are also commonly used to form whitelists to exclude known or irrelevant content, or alert lists that quickly identify known interesting files. In some cases, though, we need to identify near matches—something that our MD5, SHA1, and SHA256 algorithms can't handle on their own.

One of the most common utilities that assists with similarity analysis is ssdeep, developed by...