Generating predicted end-to-end cyber-security attack characteristics via bifurcated machine learning-based processing of multi-modal data systems and methods

A method for generating predicted end-to-end cyber-security attack characteristics via bifurcated machine learning-based processing of multi-modal data, the method comprising:

• accessing multi-modal data indicating a set of security information related to a computing system of an entity;
• applying a supervised machine learning model to the multi-modal data to generate a set of extracted characteristics indicating a cyber-security attack on the computing system of the entity,
• wherein the supervised machine learning model is trained on historical multi-modal data indicating labeled subsets of the historical multi-modal data, and
• wherein the labeled subsets of the historical multi-modal data indicate whether the subset of the historical multi-modal data indicates a cyber-security attack;
• applying an unsupervised machine learning model to the extracted set of characteristics indicating the cyber-security attack to generate a revised set of extracted characteristics indicating the cyber-security attack on the computing system of the entity,
• wherein the revised set of extracted characteristics includes at least one new characteristic that was not included in the extracted set of characteristics indicating the cyber-security attack on the computing system of the entity;
• execute a mitigation action responsive to the revised set of extracted characteristics indicating the cyber-security attack, wherein the mitigation action comprises a platform usage mitigation action, a data storage mitigation action, a cryptography mitigation action, a network communication mitigation action, or a client code integration mitigation action;
• and
• generating for display a graphical representation of the revised set of extracted characteristics indicating the cyber-security attack on the computing system of the entity.

One or more non-transitory, computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising:

• accessing multi-modal data indicating a set of security information related to a computing system of an entity;
• applying a supervised machine learning model to the multi-modal data to generate a set of extracted characteristics indicating a cyber-security attack on the computing system of the entity,
• wherein the supervised machine learning model is trained on historical multi-modal data indicating labeled subsets of the historical multi-modal data, and
• wherein the labeled subsets of the historical multi-modal data indicate whether the subset of the historical multi-modal data indicates a cyber-security attack;
• applying an unsupervised machine learning model to the extracted set of characteristics indicating the cyber-security attack to generate a revised set of extracted characteristics indicating the cyber-security attack on the computing system of the entity,
• wherein the revised set of extracted characteristics includes at least one new characteristic that was not included in the extracted set of characteristics indicating the cyber-security attack on the computing system of the entity;
• execute a mitigation action responsive to the revised set of extracted characteristics indicating the cyber-security attack, wherein the mitigation action comprises a platform usage mitigation action, a data storage mitigation action, a cryptography mitigation action, a network communication mitigation action, or a client code integration mitigation action;
• and
• generating for display a graphical representation of the revised set of extracted characteristics indicating the cyber-security attack on the computing system of the entity.