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Vulnerability Discovery Modelling for Software with Multi-versions

  • Adarsh AnandEmail author
  • Subhrata Das
  • Deepti Aggrawal
  • Yury Klochkov
Chapter
Part of the Management and Industrial Engineering book series (MINEN)

Abstract

Security vulnerabilities have been of huge concern as an un-patched vulnerability can potentially permit a security breach. Vulnerability Discovery Modelling (VDM) has been a methodical approach that has helped the developers to effectively plan for resource allocation required to develop patches for problematic software releases; and thus improving the security aspect of the software. Many researchers have proposed discovery modelling pertaining to a specific version of software and talked about time window between the discovery (of vulnerability) and release of the patch as its remedy. In today’s cut throat and neck to neck competitive market scenario, when every firm comes up with the successive version of its previous release; fixing of associated susceptibilities in the software becomes a more cumbersome task. Based on the fundamental of shared code among multi-version software system, in this chapter of the book, we propose a systematic approach for quantification of number of vulnerabilities discovered. With the aim of predicting and scrutinising the loopholes the applicability of the approach has been examined using various versions of Windows and Windows Server Operating Systems.

Keywords

Multi-version software Patch Vulnerability discovery model (VDM) 

Notation

\(\Omega _{i} (t)\)

Expected number of vulnerabilities discovered by time t(\(i = 1,{\kern 1pt} 2,{\kern 1pt} 3 \ldots n\))

\(F_{i} (t)\)

Probability distributions function for vulnerability discovery process (\(i = 1,{\kern 1pt} 2,3 \ldots n\))

\(a_{i}\)

Total number of vulnerabilities in the software (\(i = 1,{\kern 1pt} 2,3 \ldots n\))

\(b_{i}\)

Vulnerability detection rate function of software (\(i = 1,{\kern 1pt} 2,3 \ldots n\))

\(\beta_{i}\)

Learning parameter for vulnerability discovery process (\(i = 1,{\kern 1pt} 2,3 \ldots n\))

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Adarsh Anand
    • 1
    Email author
  • Subhrata Das
    • 1
  • Deepti Aggrawal
    • 2
  • Yury Klochkov
    • 3
  1. 1.Department of Operational ResearchUniversity of DelhiNew DelhiIndia
  2. 2.Keshav MahavidyalayaUniversity of DelhiNew DelhiIndia
  3. 3.St. Petersburg Polytechnic UniversitySt. PetersburgRussia

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