MQTT Anomalous Behavior Detection in IP Sensor Networks Through Convolutional Neural Networks and Traffic to Image Encoding
The result's identifiers
Result code in 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>
Alternative codes found
RIV/61989100:27740/25:10259120
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
MQTT Anomalous Behavior Detection in IP Sensor Networks Through Convolutional Neural Networks and Traffic to Image Encoding
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2025 : proceedings
ISBN
979-8-3315-0156-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
283-288
Publisher name
IEEE
Place of publication
Piscataway
Event location
Benátky
Event date
Jun 11, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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