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BitDam Deep Application Learning

August 23, 2018

BitDam Deep Application Learning

BitDam deep application learning utilizes a proprietary set of static, dynamic and crowd sourced analytics to create multi-dimensional understanding of legitimate application code flows:  

BitDam Static Application Analysis.

BitDam Static Application Analysis maps application structure forming a base skeleton for the application’s knowledge base, including a list of application modules and components (e.g Microsoft Office modules installed in the windows folder). After the static analysis, BitDam verifies the static learning using proprietary dynamic analysis tools to better understand the application’s skeleton structure to the full extent.

BitDam Dynamic Application Analysis.

BitDam Dynamic Application Analysis maps behavioural characteristics of the application’s code which are discoverable during run-time of test files, only. BitDam employs a patent-pending engine to determine baseline and deviant application behaviour to build a baseline for each application. For this phase BitDam uses a set that was previously assembled from trusted sources (eg. private sources, self created sources etc) further  populating the knowledge base that was built in the previous phase, even though the sources are trusted.  New data added to the knowledge base is subject to our stringent engine findings,  ensuring that no suspect data enters the knowledge base.

BitDam Crowd Sourcing

Ongoing benchmarking of application flows from customers, to create an anonymised pool of behaviours, constantly builds up the BitDam application knowledge base, to create a crowd sourced repository of application flows. The sources are varied, from public sources to customers sources – BitDam gathers the new flows that have not been seen in previous stages, for further examination using the engine and a researcher (in order to improve the learning algorithms).