Developing Control Operations for Information Risk Management by Formulating a Stochastic Model

Constantinos T. Artikis


Stochastic models are becoming increasingly useful for understanding or making performance evaluation of systems arising in various scientific and engineering disciplines. The present paper is mainly devoted to the explicit formulation, the theoretical investigation, and the practical interpretation of a stochastic model having the necessary advantages for the precise description and the thorough investigation of the behavior and performance of a system evolving in the environment of a random number of competing, global, and catastrophic risks. In addition, such a system incorporates its principal concepts for the strong enforcement of the crucial requirements substantially facilitating the effective use of the formulated stochastic model to the reliable development and successful implementation of vital strategic processes.


System; Stochastic model; Risk; Requirements; Information; Strategic process

Full Text:



P. Artikis, C. Artikis, C. Fountas and P. Hatzopoulos, Discrete renewal and selfdecomposable distributions in modeling information risk management operations, Journal of Statistics & Management Systems 9 (2006), 73 – 85, DOI: 10.1080/09720510.2006.10701194.

P. Artikis, Stochastic concepts facilitating strategic thinking in global risk governance, Journal of Statistics & Management Systems 17 (2014), 183 – 194, DOI: 10.1080/09720510.2014.914295.

C. Artikis, Stochastic integrals and power contractions in Bernoulli selections, Journal of Informatics and Mathematical Sciences 10 (3) (2018), 411 – 415, DOI: 10.26712/jims.v10i3.909.

Y. Asnar, P. Giorgini and J. Mylopoulos, Goal-driven risk assessment in requirements engineering, Requirements Engineering 16 (2011), 101 – 116, DOI: 10.1007/s00766-010-0112-x.

B. Boehm, Software risk management principles and practices, IEEE Software 8 (1991), 32 – 41, DOI: 10.1109/52.62930.

J. Bubenko, Extending the Scope of Information Modeling, SISU, Stockholm (1993).

E. Castillo, A. Hadi, N. Balakrishnan and T. Sarabia, Extreme Value and Related Models With Applications in Engineering and Science, Wiley Series in Probability and Statistics Wiley, Hoboken, New Jersey (2005).

P. Checkland, Systems Thinking, Systems Practice, Wiley, Chichester (1981).

S. Coles, An Introduction to Statistical Modeling of Extreme Values, Springer, London (2001).

M. Feather and S. Cornford, Quantitative risk-based requirements reasoning, Requirements Engineering 8 (2003), 248 – 265, DOI: 10.1007/s00766-002-0160-y.

D. Fred, Strategic Management: Concepts and Cases, Prentice Hall, New Jersey (2010).

A. Herny, Understanding Strategic Management, Oxford University Press, Oxford (2007).

N. Johnson, A. Kemp and S. Kotz, Univariate Discrete Distributions, Wiley, New York (2005), DOI: 10.1002/0471715816.

G. Kervern and P. Boulenger, Cindyniques: Concepts et mode d’ employ, Economica, Paris (2007).

P. Loucopoulos and V. Karakostas, System Requirements Engineering, McGraw-Hill, London (1995).

A. McNeil, B. Frey and P. Embrechts, Quantitative Risk Management: Concepts, Techniques, and Tools, Princeton University Press, Princeton (2010).

M. Pinsky and S. Karlin, An Introduction to Stochastic Modeling, 4th edition, Academic Press, Oxford (2011), DOI: 10.1016/C2009-1-61171-0.

B. Ramesh and V. Dhar, Supporting systems development by capturing deliberations during requirements engineering, IEEE Transactions on Software Engineering 18 (1992), 498 – 510, DOI: 10.1109/32.142872.

I. A. Shah, A. H. Khan and H. M. Borakat, Random translation, dilation and contraction of order statistics, Statistics & Probability Letters 92 (2014), 209 – 214, DOI: 10.1016/j.spl.2014.05.025.

F. Steutel and K. Van Harn, Infinite Divisibility of Probability Distributions on the Real Line, Marcel Dekker, New York (2004).

A. Sutcliffe, A conceptual framework for requirements engineering, Requirements Engineering 1 (1996), 170 – 189.


eISSN 0975-5748; pISSN 0974-875X