Method, systems and apparatus for intelligently emulating factory control systems and simulating response data

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:

• receiving a control signal in a manufacturing system;
• generating, by a decomposer, a pair of transformed control signal by modifying the control signal, the pair of transformed signals comprising a first transformed control signal and a second transformed control signal;
• splitting the first transformed control signal into a first duplicated transformed control signal and a second duplicated transformed control signal;
• splitting the second transformed control signal into a third duplicated transformed control signal and a fourth duplicated transformed control signal;
• restoring, by an integrator, the control signal based on the first duplicated transformed control signal back and the third duplicated transformed control signal;
• generating, by the integrator, a delta between the second duplicated transformed control signal and the fourth duplicated transformed control signal;
• analyzing, by a deep learning processor, the restored control signal, the second duplicated transformed control signal, the fourth duplicated transformed control signal, and the delta to determine if anomalous activity is present in the manufacturing system;
• and
• based on the analyzing, raising an alarm if the deep learning processor determines that the anomalous activity is present.

A system comprising:

• a processor;
• and
• a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising:
• receiving a control signal in a manufacturing system;
• generating a pair of transformed control signal by modifying the control signal, the pair of transformed signals comprising a first transformed control signal and a second transformed control signal;
• splitting the first transformed control signal into a first duplicated transformed control signal and a second duplicated transformed control signal;
• splitting the second transformed control signal into a third duplicated transformed control signal and a fourth duplicated transformed control signal;
• restoring the control signal based on the first duplicated transformed control signal back and the third duplicated transformed control signal;
• generating a delta between the second duplicated transformed control signal and the fourth duplicated transformed control signal;
• analyzing, by a deep learning processor, the restored control signal, the second duplicated transformed control signal, the fourth duplicated transformed control signal, and the delta to determine if anomalous activity is present in the manufacturing system;
• and
• based on the analyzing, raising an alarm if the deep learning processor determines that the anomalous activity is present.