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

Learning Python for Forensics

By : Preston Miller, Chapin Bryce
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Learning Python for Forensics

Learning Python for Forensics

5 (4)
By: Preston Miller, 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 (18 chapters)
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15
B. Python Technical Details
17
Index

Running the UserAssist framework


Our script is capable of parsing both Windows XP- and Windows 7-based UserAssist keys. However, let's focus our attention on the differences between the CSV and XLSX output options. Using the xlsxwriter module and seeing the output should make the advantages of writing directly to an Excel file over CSV clear. While you do lose the portability of the CSV document, you gain a lot more functionality. The following is a screenshot of running the userassist.py script against a Vista NTUSER.DAT and creating an XLSX output:

The CSV output is not capable of preserving Python objects or crafting report-ready spreadsheets. The upside of a CSV report, besides the portability, is that writing the module itself is very simple. We were able to write the main logic in just a few lines of code compared with over 100 lines for the Excel document, which clearly took more time to develop.

Being able to write a customized Excel report is great, but comes at a time cost. It might...

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Learning Python for Forensics
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