A system comprising: a memory storing processor-executable steps; and a processor to execute the processor-executable steps to cause the system to: receive a first data value of a plurality of data values from a data store, wherein the first data value is from a digital twin model of an industrial asset; determine, via a vulnerability module, whether the received at least one data value is a near boundary case or not a near boundary case; in a case it is determined the first data value is a near boundary case, generate one or more adversarial samples for the first data value; input each of the one or more adversarial samples to the digital twin model; execute the digital twin model to output a system response for each input adversarial sample; determine whether the system response to each input adversarial sample has a negative impact; in a case it is determined the system response has a negative impact for a given input adversarial sample, update a trained attack detection model with the given input adversarial sample; and generate a second decision boundary based on the updated trained attack detection model. a memory storing processor-executable steps; and a processor to execute the processor-executable steps to cause the system to: receive a first data value of a plurality of data values from a data store, wherein the first data value is from a digital twin model of an industrial asset; determine, via a vulnerability module, whether the received at least one data value is a near boundary case or not a near boundary case; in a case it is determined the first data value is a near boundary case, generate one or more adversarial samples for the first data value; input each of the one or more adversarial samples to the digital twin model; execute the digital twin model to output a system response for each input adversarial sample; determine whether the system response to each input adversarial sample has a negative impact; in a case it is determined the system response has a negative impact for a given input adversarial sample, update a trained attack detection model with the given input adversarial sample; and generate a second decision boundary based on the updated trained attack detection model. receive a first data value of a plurality of data values from a data store, wherein the first data value is from a digital twin model of an industrial asset; determine, via a vulnerability module, whether the received at least one data value is a near boundary case or not a near boundary case; in a case it is determined the first data value is a near boundary case, generate one or more adversarial samples for the first data value; input each of the one or more adversarial samples to the digital twin model; execute the digital twin model to output a system response for each input adversarial sample; determine whether the system response to each input adversarial sample has a negative impact; in a case it is determined the system response has a negative impact for a given input adversarial sample, update a trained attack detection model with the given input adversarial sample; and generate a second decision boundary based on the updated trained attack detection model.