Vulnerability discovery modeling using weibull distribution

Proceedings - International Symposium on Software Reliability Engineering, ISSRE

Volumn , Issue , 2008, Pages 299-300

  Joh, HyunChul ^a   Jinyoo, Kim ^a   Malaiya, Yashwant K ^a  

^a Colorado State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEST FIT; LOGISTIC MODELS; NEW MODEL; RELIABILITY EVALUATION; SOFTWARE SYSTEMS; VULNERABILITY DISCOVERY;

COMPUTER SOFTWARE SELECTION AND EVALUATION; HTTP; RISK ASSESSMENT; RISK MANAGEMENT; SOFTWARE RELIABILITY;

WEIBULL DISTRIBUTION;

EID: 67249141121     PISSN: 10719458     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISSRE.2008.32     Document Type: Conference Paper

References (6)

1. E. E. Schultz, Jr., D. S. Brown, and T. A. Longstaff, Responding to Computer Security Incidents. : Lawrence Livermore National Labo-ratory, July 23, 1990.
2. O. H. Alhazmi and Y. K. Malaiya, Application of Vulnerability Discovery Models to Major Operating Systems, IEEE Trans. Reliability, March 2008, pp. 14- 22.
3. Bonner, F.T., and T.R. Dell, The Weibull function: A new method of comparing seed vior, Journal of Seed Technology, 1976, pp. 96-103.
4. Boost Software. Weibull Distribution. Available: http://www.boost.org/ doc/libs/1-35-0/libs/math/doc/sf-and-dist/html/math-toolkit/dist/dist-ref/dists/ weibull.ht ml, August 10, 2008.
5. National Institute of Standards and Technology. National Vulnerability Database. Available:http://nvd. nist.gov/download.cfm, March 31, 2008.
6. Cain, S.R., Distinguishing between lognormal and Weibull distributions, IEEE Trans. Reliability, March 2002, pp. 32-38. (Pubitemid 34630920)