A Conceptual Antifragile Microservice Framework for Reshaping Critical Infrastructures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00126307" target="_blank" >RIV/00216224:14330/22:00126307 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/9977445" target="_blank" >https://ieeexplore.ieee.org/abstract/document/9977445</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICSME55016.2022.00040" target="_blank" >10.1109/ICSME55016.2022.00040</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Conceptual Antifragile Microservice Framework for Reshaping Critical Infrastructures

Popis výsledku v původním jazyce

Recently, microservices have been examined as a solution for reshaping and improving the flexibility, scalability, and maintainability of critical infrastructure systems. However, microservice systems are also suffering from the presence of a substantial number of potentially vulnerable components that may threaten the protection of critical infrastructures. To address the problem, this paper proposes to leverage the concept of antifragility built in a framework for building self-learning microservice systems that could be strengthened by faults and threats instead of being deteriorated by them. To illustrate the approach, we instantiate the proposed approach of autonomous machine learning through an experimental evaluation on a benchmarking dataset of microservice faults.

Název v anglickém jazyce

A Conceptual Antifragile Microservice Framework for Reshaping Critical Infrastructures

Popis výsledku anglicky

Recently, microservices have been examined as a solution for reshaping and improving the flexibility, scalability, and maintainability of critical infrastructure systems. However, microservice systems are also suffering from the presence of a substantial number of potentially vulnerable components that may threaten the protection of critical infrastructures. To address the problem, this paper proposes to leverage the concept of antifragility built in a framework for building self-learning microservice systems that could be strengthened by faults and threats instead of being deteriorated by them. To illustrate the approach, we instantiate the proposed approach of autonomous machine learning through an experimental evaluation on a benchmarking dataset of microservice faults.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

The 38th IEEE International Conference on Software Maintenance and Evolution

ISBN

9781665479561

ISSN

1063-6773

e-ISSN

2576-3148

Počet stran výsledku

5

Strana od-do

364-368

Název nakladatele

IEEE

Místo vydání

New York, USA

Místo konání akce

New York, USA

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000908031000032