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 JIS  Vol.7 No.4 , July 2016
Stochastic Modelling of Vulnerability Life Cycle and Security Risk Evaluation
Abstract: The objective of the present study is to propose a risk evaluation statistical model for a given vulnerability by examining the Vulnerability Life Cycle and the CVSS score. Having a better understanding of the behavior of vulnerability with respect to time will give us a great advantage. Such understanding will help us to avoid exploitations and introduce patches for a particular vulnerability before the attacker takes the advantage. Utilizing the proposed model one can identify the risk factor of a specific vulnerability being exploited as a function of time. Measuring of the risk factor of a given vulnerability will also help to improve the security level of software and to make appropriate decisions to patch the vulnerability before an exploitation takes place.
Cite this paper: Rajasooriya, S. , Tsokos, C. and Kaluarachchi, P. (2016) Stochastic Modelling of Vulnerability Life Cycle and Security Risk Evaluation. Journal of Information Security, 7, 269-279. doi: 10.4236/jis.2016.74022.
References

[1]   Kaluarachchi, P.K., Tsokos, C.P. and Rajasooriya, S.M. (2016) Cybersecurity: A Statistical Predictive Model for the Expected Path Length. Journal of information Security, 7, 112-128.
http://dx.doi.org/10.4236/jis.2016.73008

[2]   (2016) NVD, National Vulnerability Database.
http://nvd.nist.gov/

[3]   Frei, S. (2009) Security Econometrics: The Dynamics of (IN) Security. PhD Dissertation, ETH, Zurich.

[4]   Joh, H. and Malaiya, Y.K. (2010) A Framework for Software Security Risk Evaluation Using the Vulnerability Lifecycle and CVSS Metrics. Proceedings of the International Workshop on Risk and Trust in Extended Enterprises, November 2010, 430-434.

[5]   Kijsanayothin, P. (2010) Network Security Modeling with Intelligent and Complexity Analysis. PhD Dissertation, Texas Tech University, Lubbock.

[6]   Alhazmi, O.H., Malaiya, Y.K. and Ray, I. (2007) Measuring, Analyzing and Predicting Security Vulnerabilities in Software Systems. Computers and Security Journal, 26, 219-228.
http://dx.doi.org/10.1016/j.cose.2006.10.002

[7]   Schiffman, M. (2014) Common Vulnerability Scoring System (CVSS).
http://www.first.org/cvss/

[8]   Noel, S., Jacobs, M., Kalapa, P. and Jajodia, S. (2005) Multiple Coordinated Views for Network Attack Graphs. VIZSEC’05: Proceedings of the IEEE Workshops on Visualization for Computer Security, Minneapolis, October 2005, 99-106.
http://dx.doi.org/10.1109/vizsec.2005.1532071

[9]   Mehta, V., Bartzis, C., Zhu, H., Clarke, E.M. and Wing, J.M. (2006) Ranking Attack Graphs. In: Zamboni, D. and Krügel, C., Eds., Recent Advances in Intrusion Detection, Volume 4219, Lecture Notes in Computer Science, Springer, Berlin, 127-144.
http://dx.doi.org/10.1007/11856214_7

[10]   Alhazmi, O.H. and Malaiya, Y.K. (2008) Application of Vulnerability Discovery Models to Major Operating Systems. IEEE Transactions on Reliability, 57, 14-22.
http://dx.doi.org/10.1109/TR.2008.916872

[11]   Lawler, G.F. (2006) Introduction to Stochastic processes. 2nd Edition, Chapman and Hall/CRC Taylor and Francis Group, London, New York.

[12]   Jajodia, S. and Noel, S. (2005) Advanced Cyber Attack Modeling, Analysis, and Visualization. 14th USENIX Security Symposium, Technical Report 2010, George Mason University, Fairfax.

[13]   Abraham, S. and Nair, S. (2014) Cyber Security Analytics: A Stochastic Model for Security Quantification Using Absorbing Markov Chains. Journal of Communications, 9, 899-907.
http://dx.doi.org/10.12720/jcm.9.12.899-907

[14]   Wang, L., Singhal, A. and Jajodia, S. (2007) Measuring Overall Security of Network Configurations Using Attack Graphs. Data and Applications Security XXI, 4602, 98-112.
http://dx.doi.org/10.1007/978-3-540-73538-0_9

[15]   Wang, L., Islam, T., Long, T., Singhal, A. and Jajodia, S. (2008) An Attack Graph-Based Probabilistic Security Metric. DAS 2008, LNCS 5094, 283-296.

[16]   Alhazmi, O.H. and Malaiya, Y.K. (2005) Modeling the Vulnerability Discovery Process. Proceedings of 16th International Symposium on Software Reliability Engineering, Chicago, 8-11 November 2005, 129-138.
http://dx.doi.org/10.1109/ISSRE.2005.30

 
 
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