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In addition to the weekly signature updates from InQuest™ Labs, support for user-defined signatures is simplified within the platform. Whether you are utilizing third-party threat intel providers, company-specific data-loss signatures, or an advanced in-house signature developer, InQuest™ provides options for multi-tenant implementation of user-defined detection capability.

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Beyond the capability of identifying, extracting, and exposing malicious content from hundreds of file types. InQuest Deep File Inspection (DFI) utilizes machine vision and optical character recognition (OCR) to identify the social engineering component of a variety of malware lures.

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InQuest not only provides the ability to capture and catalog all web and e-mail session data carrier class speeds up to 20Gbps. We also support the customer with the ability to capture and analyze data-at-rest via CIFS/SMB.

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We provide the customer with the ability to perform Hunting through historical artifact queries and file analysis using our internal RetroHunt component (retrospective static file analysis).

In addition to the Deep File Inspections signatures provided by InQuest Labs, the platform provides the ability to create, test, and tune custom signatures to address threats to your organization.Retrohunt is a perfect solution to scan previous network events with emerging 0-day signatures.

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Most modern anti-malware solutions have limitations when it comes to the detection, inspection, and mitigation of embedded file content. This results from the tendency of malware to be nested in multiple layers of an application, making its detection extremely difficult. InQuest’s platform enables users to create and apply custom static analysis signatures leveraging the same performance and deep analytics benefits as the rest of the platform. This allows for multi-engine scanning using the latest information about emerging malware threats.

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Deep File Inspection, or DFI, is the reassembly of packets captured off of the wire into application level content that is then reconstructed, unraveled, and dissected (decompressed, decoded, decrypted, deobfuscated) in an automated fashion.

This allows heuristic analysis to better determine the intent by analysis of the file contents (containers, objects, etc.) as an artifact.

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