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  • Book Overview & Buying Learning Python for Forensics
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Learning Python for Forensics

Learning Python for Forensics - Second Edition

By : Preston Miller, Chapin Bryce
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Learning Python for Forensics

Learning Python for Forensics

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)
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Manually manipulating databases with Python – file_lister.py

As a note, this script will be designed to work only in Python 3 and was tested with Python 3.7.1. If you'd like the Python 2 version of the code after working through this section, please see https://github.com/PacktPublishing/Learning-Python-for-Forensics for the prior iteration.

In the first iteration of the script, we use several standard libraries to complete all of the functionality required for the full operation. Like we did in prior scripts, we are implementing argparse, csv, and logging for their usual purposes, which include argument handling, writing CSV reports, and logging program execution. For logging, we define our log handler, logger, on line 43. We have imported the sqlite3 module to handle all database operations. Unlike our next iteration, we will only support SQLite databases through...

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Learning Python for Forensics
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