Patent ID: 11962610
Assignee: EZOTECH INC.
Field: Digital communication (Electrical engineering)
Classification: CPC H  G | IPC G  H

Claim 6:
7. A method for organizing computer resources for testing a security of a target network, the target network being a computer network, the method comprising:
(a) receiving data and characteristics for said target network and storing said data and characteristics in a central data storage data hive;
(b) determining a state of said target network based on said data and characteristics stored in said data hive, wherein, at a particular point in time, said state is a binary value representing said data and characteristics at said particular point in time;
(c) formulating a plurality of potential attacks using neural networks, said potential attacks being against said target network based on said state, each of said neural networks determining a predictive score associated with a specific one of said plurality of potential attacks, said predictive score representing a likelihood that said specific one of said plurality of potential attacks will work on said target network;
(d) for each one of said plurality of potential attacks, determining if a module exists for said one of said plurality of potential attacks based on said state and on said predictive score, wherein said module comprises instructions that, when executed, execute said one of said plurality of potential attacks, said one of said plurality of potential attacks being a module-validated potential attack if said module exists for said one of said plurality of potential attacks, and said one of said plurality of potential attacks being invalid if no said module exists for said one of said plurality of potential attacks;
(e) when at least one of said plurality of potential attacks is a module-validated potential attack, determining if conditions for said module-validated potential attack are present based on said data and characteristics in said data hive, said module-validated potential attack being an executable attack if said conditions are present and said module-validated potential attack being invalid if said conditions are absent;
(f) provisioning resources for each executable attack; and
(g) executing said each executable attack, new data being generated by each said executable attack being saved in said data hive, such that said new data is preserved and available for later use by said neural networks, and such that said neural networks autonomously learn conditions in which attacks are successful.