Intelligent Technologies Research Group

Research fields

Theoretical and applied aspects of machine learning and mathematical modeling (2019–2021)
   Physical Sciences (Informatics) 41-T-12, Technological Sciences (Informatics Engineering) 42-E-4

Main goal

Develop and explore models based on machine learning and mathematical modeling for cyber security, anomalous data detection, real-time data classification, prediction, and explanation of results.

 Tasks in 2019:

• Application of machine learning in anomalous network data detection.
• Adapting heavy tail models to identify abnormalities in security tasks of computer networks.
• Modification of classification algorithms for CERN data certification.
• Critical region inequalities for Riemann zeta function modules.
• Create a recursive algorithm for the recognition of the parameters of the Wiener process monitored with noise.
• Create a recursive algorithm for multi-dimensional Dirichlet distribution parameter recognition.
• Testing and matching metrics and an agent-based model for measuring the impact of cultural processes on social capital to create a functional simulation prototype.
• Get the primary results in the theory of networks with different performance nodes.