IMPLEMENTING A MACHINE-LEARNING MODEL TO IDENTIFY CRITICAL SYSTEMS IN AN ENTERPRISE ENVIRONMENT

• Main Author: Ackerman, Karl
• Format: Patent
• Language: English
• Published: 26.10.2023
• Subjects: ELECTRIC COMMUNICATION TECHNIQUE; ELECTRICITY; TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION

A computer-implemented method includes training a machine-learning model, using a training dataset that distinguishes between critical systems and non-critical systems, to classify a particular computer system as critical or non-critical, wherein a label is applied to the particular computer system during the training that identifies the particular computer system as critical or non-critical, and wherein parameters that describe the critical systems or non-critical systems are used as features during the training. The method further includes receiving an input dataset that describes a plurality of computer systems in the enterprise environment. The method further includes outputting, using the trained machine-learning model, an identification of one or more critical systems of the plurality of computer systems within the enterprise environment and an identification of one or more non-critical systems of the plurality of computer systems within the enterprise environment, wherein each identification is associated with a confidence level.