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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

About time

Recipe Difficulty: Easy

Python Version: 2.7 or 3.5

Operating System: Any

One important element of any good log file is the timestamp. This value conveys the date and time of the activity or event noted in the log. These date values can come in many formats and may be represented as numbers or hexadecimal values. Outside of logs, different files and artifacts store dates in different manners, even if the data type remains the same. A common differentiating factor is the epoch value, which is the date that the format counts time from. A common epoch is January 1, 1970, though other formats count from January 1, 1601. Another factor that differs between formats is the interval used for counting. While it is common to see formats that count seconds or milliseconds, some formats count blocks of time, such as the number of 100-nanoseconds since the epoch. Because of this...

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