Introducing Magnet.AI: Putting Machine Learning to Work for Forensics

Child exploitation investigations often involve luring (also known as grooming): the process by which a child predator gains their victim’s trust. Because this happens a lot over chat apps (and chat features within, for example, gaming apps), you might find yourself reviewing thousands or even millions of messages to find your evidence. When you need to find relevant evidence quickly to move an investigation forward, you don’t necessarily have the time it takes to evaluate these messages individually.

That’s why Magnet Forensics has developed a new way to analyze and classify content using machine learning: Magnet.AI. Found within Magnet AXIOM 1.1, Magnet.AI uses machine learning to narrow massive amounts of content. In its initial offering, Magnet.AI focuses on chat. Its function is to suggest chat conversations that should be examined for child luring.

How Magnet.AI’s contextual content analysis differs from other analyticsWithin the generally accepted range of digital forensics or even other “predictive policing” tools, “analytics” usually refers to the tool’s ability to pinpoint data based on a date and/or time range, a set of keywords, a location grouping, or some other structured, discrete piece of data.

However, language is frequently unstructured, with nuances such as slang that keywords can confuse or miss. Such keywords can narrow an investigator’s area of focus, of course, but they lack the context to tell when a message falls into one of two categories: “is” or “is not.” You might not be able to determine intent, for example, between a “let’s meet” designed to lure a child to a motel room, versus a “let’s meet” directed at a fellow business owner.

Distinguishing between the two types of messages may take more time and effort than you have, especially if you’re faced with multiple devices and limited time to identify, interview, and arrest (or not arrest) your suspect. You need a way to triage each device, not just for contraband images, but also for the conversations that might mean the suspect is actively hunting children.


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Machine learning, via Magnet.AI, offers that context by providing a predictive apparatus “trained” to recognize messages that fall into these categories, and then to classify them accordingly as “possible luring.” This ability gives you a place to start, both for finding evidence and for using it during interviews and arrest proceedings.

To learn more about the future of machine learning in digital forensics, read the rest of the story here: www.magnetforensics.com/blog/introducing-magnet-ai-putting-machine-learning-work-forensics/

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Alan Platt, Professional Services Consultant at MSAB, discusses his experience as a former UK police officer working in digital forensics. He talks about the different levels of digital forensics capabilities within police forces and how MSAB products like XAMN and XEC Director are used by frontline officers versus lab analysts.

The discussion covers how MSAB partners with law enforcement to develop custom workflows for mobile device acquisitions that facilitate ISO compliance. Alan explains MSAB's managed service offering, where approved MSAB staff can remotely access a customer's XEC Director server to assist with software updates and troubleshooting. He emphasizes the strict data segregation policies enforced by customers to prevent MSAB from accessing any sensitive case data.

Looking ahead, Alan mentions MSAB's new CEO and hints at some exciting developments coming down the pipeline. He spotlights recent enhancements to XEC Director's speed and database functionality for managing large estates of networked Kiosks. Alan also plugs the new XEC Director training he created to help users fully leverage the platform's capabilities.

00:00 – Introduction to Alan Platt
07:00 – Training
12:00 – Workflows
17:20 – Ensuring a secure environment
19:45 – Customer training
20:35 – Helping customers comply with ISO accreditation
25:00 – Validation and verification
27:30 – ISO standards
30:00 – MSAB’s pipeline plans
32:40 – XEC Director
43:45 – Privacy of user data

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