Dynamic monitoring and securing of factory processes, equipment and automated systems

A manufacturing system, comprising:

• a process station configured to perform a step of a manufacturing process;
• a station controller programmed to control an operation of the process station;
• a deep learning controller in communication with the process station and the station controller, wherein the deep learning controller is trained to identify anomalous activity in the manufacturing process based on response data received from the station controller;
• a first signal splitter positioned between the station controller and the process station, the first signal splitter having a single input, a first output, and a second output, the first signal splitter configured to receive a control signal transmitted from the station controller to the process station, divide the control signal, and provide a first portion of the divided control signal to the deep learning controller via the first output and a second portion of the divided control signal is provided to the process station via the second output;
• and
• a second signal splitter positioned downstream of the process station, the second signal splitter having a second single input, a third output, and a fourth output, the second signal splitter configured to receive control values output by the process station, divide the control values, and provide a first portion of the divided control values to the deep learning controller via the third output and a second portion of the divided control values to the station controller via the fourth output.

A computer-implemented method in a manufacturing process comprising:

• receiving, by a deep learning controller, a first portion of a control signal from a station controller, wherein the control signal is split, by a first signal splitter having a single input, a first output, and a second output, the first signal splitter receiving the control signal from the station controller, and dividing the control signal between the first portion and a second portion, the second portion sent to a process station associated with the station controller via the first signal splitter;
• receiving, by the deep learning controller, a first portion of control values from the process station, wherein the control values are split, by a second signal splitter having a second signal input, a third output, and a fourth output, the second signal splitter receiving the control values from the process station, and dividing the control values between the first portion of the control values and a second portion of the control values, the second portion provided to the station controller;
• generate, by the deep learning controller, expected response data and expected behavioral pattern data based on the first portion of the control signal and the first portion of the control values;
• and
• identify, by the deep learning controller, whether there is anomalous activity in the manufacturing process based on at least one of the expected response data and the expected behavioral pattern data.