How To Acquire Cloud Data With MD-CLOUD

‘17.5 Zettabytes.’ This is the amount of data that the IDC estimates will be generated annually by 2025, and among those numbers, cloud traffic is expected to grow and reach 18.9 Zettabytes by 2021.

This tremendous amount of cloud data is generated and fueled in the course of building driver assistance and autonomous vehicle technologies; IoT devices including sensors in our bodies, homes, factories, and cities; high-resolution content for 360 video and augmented reality; and 5G communications globally.

As many digital forensic investigators are facing so-called ‘digital transformation’, finding evidence data from various cloud services is a highly demanding and important mission for digital forensic investigators.

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Latest Videos

Quantifying Data Volatility for IoT Forensics With Examples From Contiki OS

Forensic Focus 22nd June 2022 5:00 am

File timestamps are used by forensics practitioners as a fundamental artifact. For example, the creation of user files can show traces of user activity, while system files, like configuration and log files, typically reveal when a program was run. 

Despite timestamps being ubiquitous, the understanding of their exact meaning is mostly overlooked in favor of fully-automated, correlation-based approaches. Existing work for practitioners aims at understanding Windows and is not directly applicable to Unix-like systems. 

In this paper, we review how each layer of the software stack (kernel, file system, libraries, application) influences MACB timestamps on Unix systems such as Linux, OpenBSD, FreeBSD and macOS.

We examine how POSIX specifies the timestamp behavior and propose a framework for automatically profiling OS kernels, user mode libraries and applications, including compliance checks against POSIX.

Our implementation covers four different operating systems, the GIO and Qt library, as well as several user mode applications and is released as open-source.

Based on 187 compliance tests and automated profiling covering common file operations, we found multiple unexpected and non-compliant behaviors, both on common operations and in edge cases.

Furthermore, we provide tables summarizing timestamp behavior aimed to be used by practitioners as a quick-reference.

Learn more: https://dfrws.org/presentation/a-systematic-approach-to-understanding-macb-timestamps-on-unixlike-systems/

File timestamps are used by forensics practitioners as a fundamental artifact. For example, the creation of user files can show traces of user activity, while system files, like configuration and log files, typically reveal when a program was run.

Despite timestamps being ubiquitous, the understanding of their exact meaning is mostly overlooked in favor of fully-automated, correlation-based approaches. Existing work for practitioners aims at understanding Windows and is not directly applicable to Unix-like systems.

In this paper, we review how each layer of the software stack (kernel, file system, libraries, application) influences MACB timestamps on Unix systems such as Linux, OpenBSD, FreeBSD and macOS.

We examine how POSIX specifies the timestamp behavior and propose a framework for automatically profiling OS kernels, user mode libraries and applications, including compliance checks against POSIX.

Our implementation covers four different operating systems, the GIO and Qt library, as well as several user mode applications and is released as open-source.

Based on 187 compliance tests and automated profiling covering common file operations, we found multiple unexpected and non-compliant behaviors, both on common operations and in edge cases.

Furthermore, we provide tables summarizing timestamp behavior aimed to be used by practitioners as a quick-reference.

Learn more: https://dfrws.org/presentation/a-systematic-approach-to-understanding-macb-timestamps-on-unixlike-systems/

YouTube Video UCQajlJPesqmyWJDN52AZI4Q_i0zd7HtluzY

A Systematic Approach to Understanding MACB Timestamps on Unixlike Systems

Forensic Focus 21st June 2022 5:00 am

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