Method, systems and apparatus for intelligently emulating factory control systems and simulating response data
A simulated process is initiated. The simulated process includes generating, by an emulator, a control signal based on external inputs. The simulated process further includes processing, by a simulator, the control signal to generate simulated response data. The simulated process further includes generating, by a deep learning processor, expected behavioral pattern data based on the simulated response data. An actual process is initiated by initializing setpoints for a process station in a manufacturing system. The actual process includes generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station. The deep learning processor compares the expected behavioral pattern to the actual behavioral pattern. Based on the comparing, the deep learning processor determines that anomalous activity is present in the manufacturing system. Based on the anomalous activity being present, the deep learning processor initiates an alert protocol.
1. A method, comprising:
initiating a simulated process, wherein the simulated process comprises:
generating, by an emulator, a control signal based on external inputs,
processing, by a simulator, the control signal to generate simulated response data, and
generating, by a deep learning processor, expected behavioral pattern data based on the simulated response data;
initiating an actual process by initializing setpoints for at least one process station in a manufacturing system, wherein the actual process comprises:
generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station;
comparing, by the deep learning processor, the expected behavioral pattern data to the actual behavioral pattern data;
based on the comparing, determining, by the deep learning processor, that anomalous activity is present in the manufacturing system; and
based on the anomalous activity being present, initiating, by the deep learning processor, an alert protocol.
2. The method of claim 1 , further comprising:
based on the alert protocol, assuming, by the deep learning processor, control of the actual process.
3. The method of claim 1 , wherein initiating the alert protocol comprises:
shutting down one or more process stations at risk as a result of the anomalous activity.
4. The method of claim 1 , wherein initiating the alert protocol comprises:
generating a notification specifying the anomalous activity; and
providing the notification to an operator to review the anomalous activity.
5. The method of claim 1 , wherein determining, by the deep learning processor, that anomalous activity is present in the manufacturing system comprises:
identifying a deviation between the expected behavioral pattern data and the actual behavioral pattern data.
6. The method of claim 1 , wherein determining, by the deep learning processor, that the anomalous activity is present in the manufacturing system comprises:
generating a confidence score for the anomalous activity.
7. The method of claim 6 , wherein the confidence score dictates whether the anomalous activity is deemed malicious activity.
8. The method of claim 1 , wherein initiating the simulated process comprises:
interfacing the emulator with an external source, wherein the emulator receives the external inputs from the external source for the simulated process.
9. A system, comprising:
processor;
an emulator;
a simulator;
a deep learning processor; and
a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations, comprising:
initiating a simulated process, wherein the simulated process comprises:
generating, by the emulator, a control signal based on external inputs,
processing, by the simulator, the control signal to generate simulated response data, and
generating, by the deep learning processor, expected behavioral pattern data based on the simulated response data;
initiating an actual process by initializing setpoints for at least one process station in a manufacturing system, wherein the actual process comprises:
generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station;
comparing, by the deep learning processor, the expected behavioral pattern data to the actual behavioral pattern data;
based on the comparing, determining, by the deep learning processor, that anomalous activity is present in the manufacturing system; and
based on the anomalous activity being present, initiating, by the deep learning processor, an alert protocol.
10. The system of claim 9 , wherein the operations further comprise:
based on the alert protocol, assuming, by the deep learning processor, control of the actual process.
11. The system of claim 9 , wherein initiating the alert protocol comprises:
shutting down one or more process stations at risk as a result of the anomalous activity.
12. The system of claim 9 , wherein initiating the alert protocol comprises:
generating a notification specifying the anomalous activity; and
providing the notification to an operator to review the anomalous activity.
13. The system of claim 9 , wherein determining, by the deep learning processor, that anomalous activity is present in the manufacturing system comprises:
identifying a deviation between the expected behavioral pattern data and the actual behavioral pattern data.
14. The system of claim 9 , wherein determining, by the deep learning processor, that the anomalous activity is present in the manufacturing system comprises:
generating a confidence score for the anomalous activity.
15. The system of claim 14 , wherein the confidence score dictates whether the anomalous activity is deemed malicious activity.
16. The system of claim 9 , wherein initiating the simulated process comprises:
interfacing the emulator with an external source, wherein the emulator receives the external inputs from the external source for the simulated process.
17. A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by a processor, causes a computing system to perform operations comprising:
initiating a simulated process, wherein the simulated process comprises:
generating, by an emulator, a control signal based on external inputs,
processing, by a simulator, the control signal to generate simulated response data, and
generating, by a deep learning processor, expected behavioral pattern data based on the simulated response data;
initiating an actual process by initializing setpoints for at least one process station in a manufacturing system, wherein the actual process comprises:
generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station;
comparing, by the deep learning processor, the expected behavioral pattern data to the actual behavioral pattern data;
based on the comparing, determining, by the deep learning processor, that anomalous activity is present in the manufacturing system; and
based on the anomalous activity being present, initiating, by the deep learning processor, an alert protocol.
18. The non-transitory computer readable medium of claim 17 , further comprising:
based on the alert protocol, assuming, by the deep learning processor, control of the actual process.
19. The non-transitory computer readable medium of claim 17 , wherein initiating, by the computing system, the alert protocol comprises:
shutting down one or more process stations at risk as a result of the anomalous activity.
20. The non-transitory computer readable medium of claim 17 , wherein initiating, by the computing system, the alert protocol comprises:
generating a notification specifying the anomalous activity; and
providing the notification to an operator to review the anomalous activity.