Vulnerability Discovery Model
(Redirected from V D M)
A Vulnerability Discovery Model (VDM) uses discovery event data with software reliability models for predicting the same. A thorough presentation of VDM techniques is available in.[1] Numerous model implementations are available in the MCMCBayes open source repository. Several VDM examples include:
- Alhazmi-Malaiya: Time based model (Alhazmi-Malaiya Logistic (AML) model)[2]
- Alhazmi-Malaiya: Effort based model[2]
- Rescorla: Quadratic Model and Exponential Model [3]
- Anderson: Thermodynamic Model[4]
- Kim: Weibull Model[5]
- Linear Model
- Hump-Shaped Model[6]
- Independent and Dependent Model[7]
- Vulnerability Discovery Modeling using Bayesian model averaging[8]
- Multivariate Vulnerability Discovery Models [9]
See also
[edit]- Attack (computing)
- Computer security
- Information security
- IT risk
- Threat (computer)
- Vulnerability (computing)
References
[edit]- ^ Johnston, Reuben (August 31, 2018). A Multivariate Bayesian Approach to Modeling Vulnerability Discovery in the Software Security Lifecycle (PhD). The George Washington University.
- ^ a b O. H. Alhazmi and Y. K. Malaiya, “Quantitative vulnerability assessment of systems software,” in Proc. Annual Reliability and Maintainability Symposium, January 2005, pp. 615–620.
- ^ E. Rescola, “Is finding security holes a good idea?,” Security and Privacy, pp. 14–19, Jan./Feb. 2005.
- ^ R. J. Anderson, “Security in open versus closed systems—The dance of Boltzmann, Coase and Moore,” in Open Source Software: Economics, Law and Policy. Toulouse, France, June 20–21, 2002.
- ^ HyunChul Joh, Jinyoo Kim, Yashwant K. Malaiya, "Vulnerability Discovery Modeling Using Weibull Distribution," issre, pp. 299–300, 2008 19th International Symposium on Software Reliability Engineering, 2008.
- ^ Anand, Adarsh; Bhatt, Navneet (2016-05-12). "Vulnerability Discovery Modeling and Weighted Criteria Based Ranking". Journal of the Indian Society for Probability and Statistics. 17 (1): 1–10. doi:10.1007/s41096-016-0006-4. ISSN 2364-9569. S2CID 111649745.
- ^ "VDM" (PDF).
- ^ Johnston; et al. (March 2019). "Bayesian-model averaging using MCMCBayes for web-browser vulnerability discovery". Reliability Engineering & System Safety. 183: 341–359. doi:10.1016/j.ress.2018.11.030. S2CID 59222056.
- ^ Johnston; et al. (August 2018). "Multivariate models using MCMCBayes for web-browser vulnerability discovery". Reliability Engineering & System Safety. 176: 52–61. doi:10.1016/j.ress.2018.03.024. S2CID 49323550.