Performance Analysis of Neural Network Approach for Evaluation of Trust in Ad-Hoc Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00351927" target="_blank" >RIV/68407700:21240/21:00351927 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9548546" target="_blank" >https://ieeexplore.ieee.org/document/9548546</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACIT52158.2021.9548546" target="_blank" >10.1109/ACIT52158.2021.9548546</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Performance Analysis of Neural Network Approach for Evaluation of Trust in Ad-Hoc Networks

Popis výsledku v původním jazyce

With the world becoming more mobile and dynamic each year, the application of ad-hoc networks has broadened. Ad-hoc networks do not have a predefined infrastructure; each node serves as a router, bringing security challenges. Trust and trustworthiness mechanisms are among the most common methods for ensuring security in an ad-hoc network. In [1], we proposed a method for the evaluation of trust in ad-hoc networks. This paper aims to describe the method formally and analyze its performance. The original paper showed that neural networks could do trust estimation with an average 98% accuracy of the classification and 94% of the regression problem. This paper aims to investigate the capabilities of our method under malicious conditions. The analysis could also provide insight for tuning trust parameters, such as the threshold of trust. Furthermore, this paper presents a mathematical model behind the problem to show that the neural network approach is reasonable.

Název v anglickém jazyce

Performance Analysis of Neural Network Approach for Evaluation of Trust in Ad-Hoc Networks

Popis výsledku anglicky

With the world becoming more mobile and dynamic each year, the application of ad-hoc networks has broadened. Ad-hoc networks do not have a predefined infrastructure; each node serves as a router, bringing security challenges. Trust and trustworthiness mechanisms are among the most common methods for ensuring security in an ad-hoc network. In [1], we proposed a method for the evaluation of trust in ad-hoc networks. This paper aims to describe the method formally and analyze its performance. The original paper showed that neural networks could do trust estimation with an average 98% accuracy of the classification and 94% of the regression problem. This paper aims to investigate the capabilities of our method under malicious conditions. The analysis could also provide insight for tuning trust parameters, such as the threshold of trust. Furthermore, this paper presents a mathematical model behind the problem to show that the neural network approach is reasonable.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

11th International Conference on Advanced Computer Information Technologies (ACIT)

ISBN

978-1-6654-1854-6

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

691-695

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

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Místo konání akce

Deggendorf

Datum konání akce

15. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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