Reinforcement-based system and method for detecting system vulnerabilities
AssigneeRoyal Bank of Canada
InventorCory FONG
Life of this patent
Estimating the term…
Abstract
A system and method for testing system vulnerabilities are provided. The method may include: training, by a processor, a machine learning model and agent to generate a payload to test vulnerabilities in the application by: selecting one or more input data from the action space to form an action data; electronically providing the action data as an input to the application; monitoring utilization of one or more system resources in response to the action data being inputted to the application; determining a score of utilization based on a result of the monitoring; determining a corresponding reward score for the action data based on the score of utilization; and identifying one or more of the action data to form a pool of candidate data for the application.
Claims20
A system for detecting vulnerabilities for a computer application, the system comprising:
- a processor;
- a non-transitory computer memory storing instructions and data representing a machine learning model, wherein the instructions when executed, configures the processor to:
- use the machine learning model to generate a payload to test vulnerabilities in an application, the machine learning model being trained based on an action space comprising one or more input data, wherein a machine learning agent is configured by the processor to:
- select one or more input data from the action space to form an action data;
- electronically provide the action data as an input to the application;
- monitor utilization of one or more system resources in response to the action data being inputted to the application;
- determine a score of utilization based on a result of the monitoring;
- determine a corresponding reward score for the action data based on the score of utilization;
- and
- identify one or more of the action data to form a pool of candidate data for the application, wherein each of action data has a corresponding reward score over a specific threshold.
A computer-implemented method for detecting vulnerabilities for a computer application, the method comprising:
- training, by a processor, a machine learning model to generate a payload to test vulnerabilities in the application, the machine learning model being trained based on an action space comprising one or more input data;
- and
- executing, by the processor, instructions to cause a machine learning agent to:
- select one or more input data from the action space to form an action data;
- electronically provide the action data as an input to the application;
- monitor utilization of one or more system resources in response to the action data being inputted to the application;
- determine a score of utilization based on a result of the monitoring;
- determine a corresponding reward score for the action data based on the score of utilization;
- and
- identify one or more of the action data to form a pool of candidate data for the application, wherein each of action data has a corresponding reward score over a specific threshold.
Dependencies are inferred from the claim text.