New Article: Learning from Other’s Mistakes – Issues Arising from E-Discovery

Forensic investigations are often conducted under limited resource availability such as time, equipment, and people.

As data acquisition is resource-demanding already, a higher emphasis needs to be put on prioritizing the investigative steps to optimize the probability of collecting the relevant evidence.

Data volatility measures how quickly data disappears from a system and is an essential part of assessing the likelihood of collecting the most valuable evidence.

An investigator can use a model for the volatility to estimate the probability of the existence of evidence.

This work motivates and details a model for data volatility and exemplifies it for the Coffee File System used in Contiki OS, an operating system for IoT devices.

Researchers conducted experiments to test how well the model corresponds to the collected simulated data and cross-validate the model with observations from file system operations.

The results revealed that an approximated model based on the known workings of the file system underestimated the volatility.

While there are many sources describing volatility qualitatively, there is little research on quantitative volatility, and this paper is a stepping stone to understanding a quantitative approach to evidence volatility.

For more information see https://dfrws.org/presentation/quantifying-data-volatility-for-iot-forensics-with-examples-from-contiki-os/

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/

Digital Forensics is 40 years old; so are the methods. Cyber attacks are happening every second while we are waiting for hours to complete for a disk duplicator to finish. A single disk from a single workstation that will be investigated by a single investigator.

Is it the correct way of doing forensics in 2022? Should we keep using the traditional methods or find alternative solutions? If yes, where is the line?

In this talk, Binalyze founder / CEO Emre Tinaztepe discusses the history of digital forensics in an effort to understand it better, highlighting both strengths and weaknesses before introducing the next era of digital forensics: Enterprise Forensics.