The Bring Your Own Device (BYOD) phenomenon is affecting forensic data acquisition because it creates crossover between data that is controlled by an individual versus by a company. People are using their personal devices for work-related tasks because it can seem easier than trying to use typical work resources.
Forensic image processing (FIP) involves the computer restoration and enhancement of surveillance imagery. The goal of FIP is to maximize information extraction from surveillance imagery, especially imagery that is noisy, incomplete, or over/under exposed. FIP techniques can be applied to various types of images, such as retinal images, shoe impression images, UAV (unmanned aerial vehicle) infrared images, and more.
Studies have shown that individuals are notoriously bad at remembering details about past events. Without replenishing or review of perceptions, neural traces in the brain degrade and information is lost. This article will examine how the use of digital forensics can aid the legal profession with fact finding to support or refute eye witness testimony involving details of events.
By now most of you will have read about the Heartbleed bug, a major vulnerability in OpenSSL. Heartbleed results from improper input validation (due to a missing bounds check) in the implementation of the TLS heartbeat extension. Heartbleed presents an interesting forensic challenge because there is unlikely to be any indication that a data breach occurred.
What happens when a smartphone is locked and unsupported by forensic tools? Flasher box, JTAG, or chip-off extraction methods become necessary. All three enable physical extraction — a logical examination cannot be performed on an unsupported locked device. However, even this capability can be limited.
This can be thought of as the measurable actions of employees in relation to safety in their work. Performance measurement should reflect how workers (management and workers alike) are actually doing compared to applicable regulatory requirements and identified corporate goals.
When you find dust fingerprints and footprints, collect them with an electrostatic dust lifter such as the PathFinder.
Boot loaders are currently considered the most forensically sound physical extraction method. While they do involve loading a piece of code onto the device, this happens before the forensic tool accesses any evidentiary data.
For nearly 25 years, advocacy groups and legal scholars have been predicting that the day when the DNA features used in forensic identification will reveal predispositions to diseases or behavioral traits is just around the corner. Questions such as these were mooted by a panel at ISHI last October. Although I cannot presume to speak for the other panelists, I can offer three recommendations of my own.
For the next few weeks, we'll present you with a list of commonly found “beastly” employees, with descriptions of their behavior and suggested strategies for coping with them. Mocking Birds will speak authoritatively on any subject— whether or not they know anything about it. They have an overwhelming need to be admired and respected and may not know they’re not experts.
Most people think you can’t get prints from a wet surface, but you can if you use Small Particle Reagent (SPR).
For the digital crimes of today, specialists need to examine a much more complex environment. Investigators need to image digital media of a multitude of types: magnetic, solid-state, or optical, for example.
I often ask my students which device is more accurate, a Rolatape, a steel tape, a handheld electronic measuring device, a total station, or a laser scanner. Ultimately that is a trick question. It is not that one device is more accurate than another. They all have the potential to measure accurately. This article is going to discuss a review of the various measuring tools used when “hand” measuring a scene.
To determine the amount of light needed in a space, designers turn to the standards developed by the Illuminating Engineering Society (IES).
Apps, not just available for iPhone or Android but also through device vendors like Samsung, Nokia, and LG — as well as from mobile carriers like T-Mobile and retailers like Amazon — are a digital forensics challenge.