Call for Paper

CAE solicits original research papers for the July 2023 Edition. Last date of manuscript submission is June 30, 2023.

Read More

Performance Prediction Model for Network Security Risk Management

Akinyemi Bodunde Odunola, Amoo Adekemi Olawumi, Aderounmu Ganiyu Adesola. Published in Security.

Communications on Applied Electronics
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Akinyemi Bodunde Odunola, Amoo Adekemi Olawumi, Aderounmu Ganiyu Adesola

Akinyemi Bodunde Odunola, Amoo Adekemi Olawumi and Aderounmu Ganiyu Adesola. Article: Performance Prediction Model for Network Security Risk Management. Communications on Applied Electronics 2(8):1-7, September 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Akinyemi Bodunde Odunola and Amoo Adekemi Olawumi and Aderounmu Ganiyu Adesola},
	title = {Article: Performance Prediction Model for Network Security Risk Management},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {2},
	number = {8},
	pages = {1-7},
	month = {September},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Network security risks compromises network asset, resources, rules, policy and guidelines. These compromises adversely affect the network performances by altering or tampering with the three set network security objectives i.e. Confidentiality, Integrity, and Availability (CIA) of network. The overall goal of network management is to maximize network performance. The proactive management of security risk of a network is thus a necessity requirement for effective and efficient performances of the network. This study aimed at developing a framework that automatically performs predictions on network security situations. This study presented a prediction model based on Bayesian Network applied to predetermine the effect of network security risk factors on the network Confidentiality, Integrity and Availability. The proposed model utilized the probability characteristics of Artificial Intelligence method to address the challenges being faced by network administrators in using objective metrics to measure their network security and justify the performance of their network, rather than relying on their instinct or experience. The proposed scheme measures the security risk quantitatively and predicts network performances using objectives metrics.


  1. Akinyemi Bodunde Odunola, Amoo Adekemi Olawumi and Olajubu Emmanuel Ajayi 2014. "An Adaptive Decision-Support Model for Data Communication Network Security Risk Management". International Journal of Computer Applications 106(8):1-7.
  2. Boudaoud, K., Boutaba, R. and Guessoum Z. 2000. Network Security management with Intelligent Agents. Network Operations and Management Symposium (NOMS 2000). 579-592.
  3. Paokanta, P. and Harnpornchai, N. 2009. Construction of Bayesian Networks for Risk Assessment of Software Project by Knowledge Engineering. 3rd International Conference on Software, Knowledge, Information Management and Applications. 154-158.
  4. García, P., Amandi, A., Schiaffino, S. and Campo, M. 2007. Evaluating Bayesian Networks' Precision for Detecting Students' Learning Styles. "Computers & Education". 49:794–808.
  5. Sun, S., Zhang, C. and Yu, G. 2006. A Bayesian Network Approach to Traffic Flow Forecasting. "IEEE Transactions on Intelligent Transportation Systems". 7(1):124-132.
  6. Pollino C.A, Woodberry O., Nicholson A., Korb K. Hart B.T. 2007. Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental Modelling & Software. 22(8): 1140–1152
  7. Xie, P., Li, J. H., Ou, X., Liu, P. and Levy, R. 2010. Using Bayesian Networks for Cyber Security Analysis. In Proceedings of the 40th IEEE/IFIP International Conference on Dependable Systems and Networks. 211-220.
  8. Wei-wei, X. and Hai-feng, W. 2010. Prediction Model of Network Security Situation Based on Regression Analysis. In proceeding of: Proceedings of the IEEE International Conference on Wireless Communications, Networking and Information Security (WCNIS). 616-619.
  9. Poolsappasit, N., Dewri, R. and Ray, I. 2012. Dynamic Security Risk Management Using Bayesian Attack Graphs. IEEE Transactions on Dependable and Secure Computing, 9(1):61-74.
  10. Pearl, J. 1999. Probabilistic Reasoning in Intelligent Systems. Networks of Plausible Inference. Morgan Kaufman.
  11. Clemen, R. T. and Winkler R. L. 1999. Combining Probability Distributions from Experts in Risk Analysis. Risk Analysis, 19(2):187-203.


Prediction, Network Performance, Bayesian Network