Anomalous behaviors detection of IoT devices through AI-based methodologies

The rapid growth of Internet of Things (IoT) devices across sectors such as healthcare, manufacturing, and smart cities has substantially increased data volume and complexity, necessitating robust anomaly detection systems to identify critical issues, including system failures, security breaches, an...

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Bibliographic Details
Published in2024 7th International Conference of Computer and Informatics Engineering (IC2IE) pp. 1 - 8
Main Authors Asghari, Ali, Bergantin, Fulvio, Forestiero, Agostino, Macri, Davide
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.09.2024
Subjects
anomalous behaviors
Anomaly detection
autoencoders
Data models
Internet of Things
Manufacturing
Medical services
Object recognition
Performance evaluation
Security
Smart cities
Transfer learning
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DOI10.1109/IC2IE63342.2024.10748216

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Summary:The rapid growth of Internet of Things (IoT) devices across sectors such as healthcare, manufacturing, and smart cities has substantially increased data volume and complexity, necessitating robust anomaly detection systems to identify critical issues, including system failures, security breaches, and inefficiencies. Traditional anomaly detection methods often struggle with the high-dimensional and dynamic nature of IoT data. This paper introduces an AI-based approach that combines deep autoencoders and transfer learning to enhance anomaly detection in IoT device behaviors. The autoencoders effectively learn detailed representations of normal operational data, enabling the identification of anomalies through significant reconstruction errors. To address the challenges posed by diverse IoT environments and limited labeled anomaly data, a transfer learning mechanism is employed to transfer knowledge from data-rich domains to those with scarce labeled data, thereby improving model generalizability and reducing the need for extensive labeled datasets. The proposed approach was evaluated using the NbaIoT dataset, demonstrating superior performance compared to traditional methods, with values of more than 90% in terms of Precision, Recall, and F-measure. These results indicate that the autoencoder-based transfer learning approach offers a scalable, adaptable, and highly accurate solution tailored to the unique characteristics of various IoT domains.
DOI:10.1109/IC2IE63342.2024.10748216