Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning Thesis

short description

  • Master's thesis

Thesis author

  • Tristancho Muñoz, Miguel Angel

abstract

  • The growing development of computer networks associated with industrial systems and their integration with corporate networks (Internet) have made this group a desired target for cybercriminals worldwide. Mitigating this type of risk is one of the highest priorities for integrators, manufacturers, and users of control systems due to the great impact that can occur on the economy, the environment and the people in an organization when materialization occurs. of an attempted attack or sabotage of industrial processes. It is becoming increasingly important for industrial organizations to become aware of the weakness of these systems and seek organizational structures for security management that help them optimize their protection against external threats from all points of view to detect and address incidents. security-related issues before they become a major problem.

publication date

  • March 24, 2023 9:27 PM

keywords

  • Cibersecurity
  • Machine Learning

Document Id

  • df2d1a24-ace7-43a8-be0c-9d5c4b2c69a6