Book Image

Practical Windows Forensics

Book Image

Practical Windows Forensics

Overview of this book

Over the last few years, the wave of the cybercrime has risen rapidly. We have witnessed many major attacks on the governmental, military, financial, and media sectors. Tracking all these attacks and crimes requires a deep understanding of operating system operations, how to extract evident data from digital evidence, and the best usage of the digital forensic tools and techniques. Regardless of your level of experience in the field of information security in general, this book will fully introduce you to digital forensics. It will provide you with the knowledge needed to assemble different types of evidence effectively, and walk you through the various stages of the analysis process. We start by discussing the principles of the digital forensics process and move on to show you the approaches that are used to conduct analysis. We will then study various tools to perform live analysis, and go through different techniques to analyze volatile and non-volatile data.
Table of Contents (20 chapters)
Practical Windows Forensics
About the Authors
About the Reviewers

Network data collection

All data that can be retrieved from the network traffic can be divided into several levels:

  • Full Packet Capture 100%

  • Packet String Data 4%

  • Sessions 0.1%

  • Statistics

  • Logs

It is obvious that, from the point of view of a forensics analyst, the most preferred method is to collect full traffic, as in this case, we obtain the most complete dataset.

However, along with the obvious advantages, this approach has a number of drawbacks. A large amount of data for storage and subsequent analysis requires a lot of time and resources.

At the same time, other forms of data, such as NetFlow, in many cases is a reasonable alternative, and it requires fewer resources for the collection and storage and to process.

Compared to other forms of full traffic, data altogether constitutes only a few percent. It require less space for storage and, therefore, can be stored for a longer time period.

For clarity, consider the following example. Let's suppose an organization has a daily volume of network...