Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent System
Analyzing IoT data is an important challenge in the smart cities domain due to the complexity of network traffic generated by a large number of interconnected devices: smart cameras, light bulbs, motion sensors, voice assistants, and so on. To overcome this issue, a multi-agent system is proposed to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/7886 |
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| author | Maria Viorela Muntean |
| author_facet | Maria Viorela Muntean |
| author_sort | Maria Viorela Muntean |
| collection | DOAJ |
| description | Analyzing IoT data is an important challenge in the smart cities domain due to the complexity of network traffic generated by a large number of interconnected devices: smart cameras, light bulbs, motion sensors, voice assistants, and so on. To overcome this issue, a multi-agent system is proposed to deal with all machine learning steps, from preprocessing and labeling data to discovering the most suitable model for the analyzed dataset. This paper shows that dividing the work into different tasks, managed by specialized agents, and evaluating the discovered models by an Expert System Agent leads to better results in the learning process. |
| format | Article |
| id | doaj-art-94e13d68712541d49a5fcbd5f95e786c |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-94e13d68712541d49a5fcbd5f95e786c2025-08-20T02:57:01ZengMDPI AGSensors1424-82202024-12-012424788610.3390/s24247886Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent SystemMaria Viorela Muntean0Department of Informatics, Mathematics and Electronics, 1 Decembrie 1918 University of Alba Iulia, 510009 Alba Iulia, RomaniaAnalyzing IoT data is an important challenge in the smart cities domain due to the complexity of network traffic generated by a large number of interconnected devices: smart cameras, light bulbs, motion sensors, voice assistants, and so on. To overcome this issue, a multi-agent system is proposed to deal with all machine learning steps, from preprocessing and labeling data to discovering the most suitable model for the analyzed dataset. This paper shows that dividing the work into different tasks, managed by specialized agents, and evaluating the discovered models by an Expert System Agent leads to better results in the learning process.https://www.mdpi.com/1424-8220/24/24/7886IoT datacybersecuritysmart citymulti-agent systemlearning modeldecision rules |
| spellingShingle | Maria Viorela Muntean Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent System Sensors IoT data cybersecurity smart city multi-agent system learning model decision rules |
| title | Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent System |
| title_full | Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent System |
| title_fullStr | Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent System |
| title_full_unstemmed | Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent System |
| title_short | Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent System |
| title_sort | real time detection of iot anomalies and intrusion data in smart cities using multi agent system |
| topic | IoT data cybersecurity smart city multi-agent system learning model decision rules |
| url | https://www.mdpi.com/1424-8220/24/24/7886 |
| work_keys_str_mv | AT mariaviorelamuntean realtimedetectionofiotanomaliesandintrusiondatainsmartcitiesusingmultiagentsystem |