Machine Learning-Assisted Cryptographic Security: A Novel ECC-ANN Framework for MQTT-Based IoT Device Communication

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F25%3A43909973" target="_blank" >RIV/60076658:12220/25:43909973 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2079-3197/13/10/227" target="_blank" >https://www.mdpi.com/2079-3197/13/10/227</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/computation13100227" target="_blank" >10.3390/computation13100227</a>

Result language

angličtina

Original language name

Machine Learning-Assisted Cryptographic Security: A Novel ECC-ANN Framework for MQTT-Based IoT Device Communication

Original language description

The Internet of Things (IoT) has surfaced as a revolutionary technology, enabling ubiquitous connectivity between devices and revolutionizing traditional lifestyles through smart automation. As IoT systems proliferate, securing device-to-device communication and server–client data exchange has become crucial. This paper presents a novel security framework that integrates elliptic curve cryptography (ECC) with artificial neural networks (ANNs) to enhance the Message Queuing Telemetry Transport (MQTT) protocol. Our study evaluated multiple machine learning algorithms, with ANN demonstrating superior performance in anomaly detection and classification. The hybrid approach not only encrypts communications but also employs the optimized ANN model to detect and classify anomalous traffic patterns. The proposed model demonstrates robust security features, successfully identifying and categorizing various attack types with 90.38% accuracy while maintaining message confidentiality through ECC encryption. Notably, this framework retains the lightweight characteristics essential for IoT devices, making it especially relevant for environments where resources are constrained. To our knowledge, this represents the first implementation of an integrated ECC-ANN approach for securing MQTT-based IoT communications, offering a promising solution for next-generation IoT security requirements.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

Computation

ISSN

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e-ISSN

2079-3197

Volume of the periodical

13

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

23

Pages from-to

"1–23"

UT code for WoS article

001602348000001

EID of the result in the Scopus database

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