Bayesian Belief Network Analysis of Computer Network Traffic Supporting Cybersystem Protection

Bayesian Belief Network Analysis of Computer Network Traffic Supporting Cybersystem Protection

Presented at the 9th Annual BayesiaLab Conference on October 14, 2021.


Comprehension of computer network traffic structure is an important part of geo-intelligence information technology’s quest to safeguard computer systems that support important areas of research and defense. Of crucial importance is the need to understand the structure of computer network traffic patterns, which are crucial to building complex algorithms that defend against computer system intrusion and attack. Bayesian belief networks and machine learning are applied to open-source computer network traffic data to develop algorithms relevant to exhuming latent patterns and modeling computer server state changes. In particular, manifold learning and Bayesian statistical methods are applied to a multidimensional data set to explore whether a two-tier analytical approach based on statistical characterization and modeling is appropriate. Preliminary statistical analysis shows which server sites from a ten-dimensional array experience a high probability of attack. Results also reveal a pattern where certain computer server sites are connected, which in turn provides a guide as to where cyber security resources should be placed to support computer network health. The structural simplicity of the developed algorithmic array offers a rigorous but flexible methodology applicable to a variety of cyber defense systems.

Presentation Video

About the Authors

Nicholas V. Scott, Ph.D.
Riverside Research Institute
Open Innovation Center
2640 Hibiscus Way
Beavercreek, OH 45431

Dr. Nicholas Scott is a modeling scientist and physical oceanographer and has been a member of the professional staff at Riverside Research in Dayton, OH, since October 2012. He investigates the applicability of traditional and non-traditional signal and image processing techniques to extracting information from remotely sensed imagery. This includes hyperspectral and multispectral imagery. His present work includes statistical modeling of geo-intelligence information, sensor array time series analysis of environmental data, and applying pattern recognition techniques to turbulent flow imagery and numerically simulated data. He is also involved in applying probabilistic graphical modeling algorithms for information fusion and statistical inference. Jack McCarthy
Duke University, Dept. of Statistical Science

Previous Conference Presentations

  • Spatio-temporal Multicomponent Optimal Learning State Estimation of Direct Numerically Simulated Turbulent Features: A Smart Sensing Approach (Laval Virtual World, 2020)
  • Bayesian Structural Field Analysis (Durham, 2019)
  • Bayesian Network Modeling of Imagery Features From Direct Numerically Simulated Turbulent Sediment-Laden Oscillatory Flow (Chicago, 2018)

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