Study of the antivirus patch testing problem through optimal control modeling

The lag of antivirus (AV) software development relative to malware development makes it necessary to constantly release AV patches. In practice, an AV patch can be deployed on an organization's intranet only when it passes compatibility test. In this context, a subset of hosts may be assigned to perform the test. The function of the fraction of the assigned hosts with respect to time is referred to as an AV patch testing (AVPT) policy, and the problem of finding a satisfactory AVPT policy in terms of the cost benefit is referred to as the AVPT problem. This paper addresses the AVPT problem through optimal control modeling. A new mathematical model of characterizing the evolution of the intranet's expected state is introduced by incorporating the effect of AV patch testing. On this basis, the AVPT problem is modeled as an optimal control problem (the AVPT model). By applying the Pontryagin Maximum Principle to this model, an iterative algorithm of solving the model is presented. The usability of the algorithm, including its convergence and effectiveness, is validated. Finally, the effect of a pair of controllable factors is inspected. This work initiates the study of patch testing-related issues through optimal control modeling.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-05-06
DOI
https://doi.org/10.1371/journal.pone.0319916
Citations
2
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
2.97
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Study of the antivirus patch testing problem through optimal control modeling

Lu‐Xing Yang, Guofang Liu, Chunlong Fu, Yang Qin et al.
2 citations
PLoS ONE
Advanced Malware Detection Techniques
2.97
article

Study of the antivirus patch testing problem through optimal control modeling

Lu‐Xing Yang, Guofang Liu, Chunlong Fu, Yang Qin, Xiaofan Yang, Yanhua Feng
article en
2 citations

Abstract

The lag of antivirus (AV) software development relative to malware development makes it necessary to constantly release AV patches. In practice, an AV patch can be deployed on an organization's intranet only when it passes compatibility test. In this context, a subset of hosts may be assigned to perform the test. The function of the fraction of the assigned hosts with respect to time is referred to as an AV patch testing (AVPT) policy, and the problem of finding a satisfactory AVPT policy in terms of the cost benefit is referred to as the AVPT problem. This paper addresses the AVPT problem through optimal control modeling. A new mathematical model of characterizing the evolution of the intranet's expected state is introduced by incorporating the effect of AV patch testing. On this basis, the AVPT problem is modeled as an optimal control problem (the AVPT model). By applying the Pontryagin Maximum Principle to this model, an iterative algorithm of solving the model is presented. The usability of the algorithm, including its convergence and effectiveness, is validated. Finally, the effect of a pair of controllable factors is inspected. This work initiates the study of patch testing-related issues through optimal control modeling.

PLoS ONEVol. 20(5)
Deakin University (AU), Chongqing University (CN), Sichuan University (CN)
Partnerships for the goals
Openalex Percentile: Top 10%
Advanced Malware Detection Techniques
2.97
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Study of the antivirus patch testing problem through optimal control modeling — Lu‐Xing Yang, Guofang Liu, et al. · PLoS ONE (2025) | TGRS Research Map | TGRS