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Python Digital Forensics Cookbook

You're reading from   Python Digital Forensics Cookbook Effective Python recipes for digital investigations

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781783987467
Length 412 pages
Edition 1st Edition
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Authors (2):
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Chapin Bryce Chapin Bryce
Author Profile Icon Chapin Bryce
Chapin Bryce
Preston Miller Preston Miller
Author Profile Icon Preston Miller
Preston Miller
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Table of Contents (11) Chapters Close

Preface 1. Essential Scripting and File Information Recipes FREE CHAPTER 2. Creating Artifact Report Recipes 3. A Deep Dive into Mobile Forensic Recipes 4. Extracting Embedded Metadata Recipes 5. Networking and Indicators of Compromise Recipes 6. Reading Emails and Taking Names Recipes 7. Log-Based Artifact Recipes 8. Working with Forensic Evidence Container Recipes 9. Exploring Windows Forensic Artifacts Recipes - Part I 10. Exploring Windows Forensic Artifacts Recipes - Part II

Parsing IIS web logs with RegEx

Recipe Difficulty: Medium

Python Version: 3.5

Operating System: Any

Logs from web servers are very useful for generating user statistics, providing us with insightful information about the devices used and the geographical locations of the visitors. They also provide clarification to examiners looking for users attempting to exploit the web server or otherwise unauthorized use. While these logs store important details, they do so in a manner inconvenient to analyze efficiently. If you were to attempt to do so manually, the field names are specified at the top of the file and would require you to remember the order of the fields as you read through the text file. Fortunately, there is a better way. Using the following script, we show how to iterate through each line, map the values to the fields, and create a spreadsheet of properly displayed results...

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