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Study of the antivirus patch testing problem through optimal control modeling.

Guofang Liu1, Chunlong Fu1, Xiaofan Yang1,2

  • 1Department of Computer Science, Sichuan University Jinjiang College, MeiShan, Sichuan 620860, China.

Plos One
|May 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal control model for antivirus patch testing (AVPT) policies to balance costs and benefits. An iterative algorithm is presented to find effective AVPT strategies, optimizing intranet security against evolving malware threats.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Operations Research

Background:

  • Malware evolves rapidly, necessitating frequent antivirus (AV) software patches.
  • Deploying AV patches requires compatibility testing on a subset of intranet hosts.
  • The Antivirus Patch Testing (AVPT) problem seeks cost-effective testing policies.

Purpose of the Study:

  • To address the AVPT problem using optimal control modeling.
  • To develop a mathematical model for intranet state evolution during AV patch testing.
  • To establish an optimal control problem (AVPT model) for AV patch deployment.

Main Methods:

  • Introduced a novel mathematical model for intranet state evolution incorporating AV patch testing.
  • Formulated the AVPT problem as an optimal control problem (AVPT model).
  • Applied the Pontryagin Maximum Principle to derive an iterative algorithm for solving the AVPT model.

Main Results:

  • Presented and validated an iterative algorithm for the AVPT model, demonstrating convergence and effectiveness.
  • The algorithm provides a method for finding satisfactory AVPT policies.
  • Inspected the impact of controllable factors on AVPT policy outcomes.

Conclusions:

  • This work pioneers the application of optimal control modeling to AV patch testing issues.
  • The developed model and algorithm offer a framework for optimizing AV patch deployment strategies.
  • The findings contribute to improving cybersecurity resilience in organizational networks.