MQTT Anomalous Behavior Detection in IP Sensor Networks Through Convolutional Neural Networks and Traffic to Image Encoding

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10259120" target="_blank" >RIV/61989100:27240/25:10259120 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/25:10259120

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/11106974" target="_blank" >https://ieeexplore.ieee.org/abstract/document/11106974</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MetroLivEnv64961.2025.11106974" target="_blank" >10.1109/MetroLivEnv64961.2025.11106974</a>

Jazyk výsledku

angličtina

Název v původním jazyce

MQTT Anomalous Behavior Detection in IP Sensor Networks Through Convolutional Neural Networks and Traffic to Image Encoding

Popis výsledku v původním jazyce

The rapid growth of Internet of Things (IoT) devices in sectors like healthcare, manufacturing, and smart cities has resulted in a substantial increase in data volume and complexity. This requires robust anomaly detection systems to identify critical issues such as system failures, security breaches, external attacks, and inefficiencies. However, traditional anomaly detection methods often struggle with the high-dimensional and dynamic nature of IoT data. In this paper we propose a new and unconventional approach for anomaly detection, cyber-attacks in particular, in IP sensor networks, based on encoding IP packets into image and exploiting the huge and well-known classification strength of Convolutional Neural Networks for malicious behavior recognition. Simulation results show the optimality of the proposed machine learning approach, outperforming the existing tools in terms of accuracy, time and complexity. © 2025 Elsevier B.V., All rights reserved.

Název v anglickém jazyce

MQTT Anomalous Behavior Detection in IP Sensor Networks Through Convolutional Neural Networks and Traffic to Image Encoding

Popis výsledku anglicky

The rapid growth of Internet of Things (IoT) devices in sectors like healthcare, manufacturing, and smart cities has resulted in a substantial increase in data volume and complexity. This requires robust anomaly detection systems to identify critical issues such as system failures, security breaches, external attacks, and inefficiencies. However, traditional anomaly detection methods often struggle with the high-dimensional and dynamic nature of IoT data. In this paper we propose a new and unconventional approach for anomaly detection, cyber-attacks in particular, in IP sensor networks, based on encoding IP packets into image and exploiting the huge and well-known classification strength of Convolutional Neural Networks for malicious behavior recognition. Simulation results show the optimality of the proposed machine learning approach, outperforming the existing tools in terms of accuracy, time and complexity. © 2025 Elsevier B.V., All rights reserved.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

2025 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2025 : proceedings

ISBN

979-8-3315-0156-3

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

283-288

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Benátky

Datum konání akce

11. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—