Scanning and detecting threats in machine learning models
A machine learning model is scanned to detect actual or potential threats. The threats can be detected before execution of the machine learning model or during an isolated execution environment. The threat detection may include performing a machine learning file format check, vulnerability check, tamper check, and stenography check. The machine learning model may also be monitored in an isolated environment during an execution or runtime session. After performing a scan, the system can generate a signature based on actual, potential, or absence of detected threats.
1. A method for scanning a machine learning model for threats comprising
receiving, by a scanning module, data for a machine learning model which is associated with model parameters and is received before execution of the machine learning model;
performing, by the scanning module, a plurality of checks based on the received machine learning model data, the checks performed while the machine learning model is not executing;
identifying, by the scanning module, whether the machine learning model includes a threat within the machine learning model based on results of the plurality of checks;
generating a signature based on identifying that the machine learning model does not include a threat; and
embedding the generated signature within the data for the machine learning model.
2. The method of claim 1 further comprising:
reporting, by the scanning module, the results to a remote computing device.
3. The method of claim 1 , wherein the plurality of checks includes two or more of a file format check, a vulnerability check, a tampering check, and a stenography check.
4. The method of claim 1 further comprising:
executing the machine learning model using the received data in an isolated environment;
monitoring the execution of the machine learning model in the isolated environment to detect suspicious activity.
5. The method of claim 1 , wherein performing a plurality of checks comprises:
determining an expected entropy for the machine learning model;
determining the actual entropy for the machine learning model;
calculating the difference between the expected entropy and the actual entropy; and
identifying a threat if the difference in expected entropy and actual entropy exceeds a threshold.
6. The method of claim 1 , wherein an identified threat may be a potential threat or an actual threat.
7. The method of claim 1 , wherein the scanning module is stored on a server forming part of a computing environment that includes the machine learning model.
8. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to perform operations for scanning a machine learning model for threats, the operations comprising:
receiving, by a scanning module, data for a machine learning model which is associated with model parameters and is received before execution of the machine learning model;
performing, by the scanning module, a plurality of checks based on the received machine learning model data, the checks performed while the machine learning model is not executing;
identifying, by the scanning module, whether the machine learning model includes a threat within the machine learning model based on results of the plurality of checks;
generating a signature based on identifying that the machine learning model does not include a threat; and
embedding the generated signature within the data for the machine learning model.
9. The non-transitory computer readable storage medium of claim 8 , wherein the operations further comprise: reporting, by the scanning module, the results to a remote computing device.
10. The non-transitory computer readable storage medium of claim 8 , wherein the plurality of checks includes two or more of a file format check, a vulnerability check, a tampering check, and a stenography check.
11. The non-transitory computer readable storage medium of claim 8 , wherein the operations further comprise: executing the machine learning model using the received data in an isolated environment, and monitoring the execution of the machine learning in the isolated environment to detect suspicious activity.
12. The non-transitory computer readable storage medium of claim 8 , wherein the operations further comprise:
determining an expected entropy for the machine learning model;
determining the actual entropy for the machine learning model;
calculating the difference between the expected entropy and the actual entropy; and
identifying a threat if the difference in expected entropy and actual entropy exceeds a threshold.
13. The non-transitory computer readable storage medium of claim 8 , wherein an identified threat may be a potential threat or an actual threat.
14. The non-transitory computer readable storage medium of claim 8 , wherein the first server which stores the scanning module is within a computing environment that includes the machine learning model.
15. A system for scanning a machine learning model for threats comprising:
at least one data processor; and
memory storing instructions which, when executed by the at least one data processor, results in operations comprising:
receiving, by a scanning module, data for a machine learning model which is associated with model parameters and is received before execution of the machine learning model;
performing, by the scanning module, a plurality of checks based on the received machine learning model data, the checks performed while the machine learning model is not executing;
identifying, by the scanning module, whether the machine learning model includes a threat within the machine learning model based on results of the plurality of checks;
generating a signature based on identifying that the machine learning model does not include a threat; and
embedding the generated signature within the data for the machine learning model.
16. The system of claim 15 , wherein the operations further comprise: reporting, by the scanning module, the results to a remote computing device.
17. The system of claim 15 , wherein the plurality of checks includes two or more of a file format check, a vulnerability check, a tampering check, and a stenography check.
18. The system of claim 15 , wherein the operations further comprise: executing the machine learning model using the received data in an isolated environment, and monitoring the execution of the machine learning model in the isolated environment to detect suspicious activity.
19. The system of claim 15 , wherein performing a plurality of checks comprises:
determining an expected entropy for the machine learning model;
determining the actual entropy for the machine learning model;
calculating the difference between the expected entropy and the actual entropy; and
identifying a threat if the difference in expected entropy and actual entropy exceeds a threshold.
20. The system of claim 15 , wherein an identified threat may be a potential threat or an actual threat.