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Multivariate Bayesian Regression Applied to the Problem of Network Security

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  • Triantafyllopoulos, Kostas
  • Pikoulas, John

Abstract

This paper examines the problem of intrusion in computer systems that causes major breaches or allows unauthorized information manipulation. A new intrusion-detection system using Bayesian multivariate regression is proposed to predict such unauthorized invasions before they occur and to take further action. We develop and use a multivariate dynamic linear model based on a unique approach leaving the unknown observational variance matrix distribution unspecified. The result is simultaneous forecasting free of the Wishart limitations that is proved faster and more reliable. Our proposed system uses software agent technology. The distributed software agent environment places an agent in each of the computer system workstations. The agent environment creates a user profile for each user. Every user has his or her profile monitored by the agent system and according to our statistical model prediction is possible. Implementation aspects are discussed using real data and an assessment of the model is provided. Copyright © 2002 by John Wiley & Sons, Ltd.

Suggested Citation

  • Triantafyllopoulos, Kostas & Pikoulas, John, 2002. "Multivariate Bayesian Regression Applied to the Problem of Network Security," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(8), pages 579-594, December.
    • Handle: RePEc:jof:jforec:v:21:y:2002:i:8:p:579-94
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    Cited by:

    1. K. Triantafyllopoulos, 2007. "Covariance estimation for multivariate conditionally Gaussian dynamic linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 551-569.
    2. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.
    3. Godolphin, E.J. & Triantafyllopoulos, Kostas, 2006. "Decomposition of time series models in state-space form," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2232-2246, May.
    4. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.
    5. Suvasini Panigrahi & Shamik Sural & Arun K. Majumdar, 2013. "Two-stage database intrusion detection by combining multiple evidence and belief update," Information Systems Frontiers, Springer, vol. 15(1), pages 35-53, March.

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