(H-DIR)<sup>2</sup>: A Scalable Entropy-Based Framework for Anomaly Detection and Cybersecurity in Cloud IoT Data Centers

(H-DIR)<sup>2</sup>: A Scalable Entropy-Based Framework for Anomaly Detection and Cybersecurity in Cloud IoT Data Centers

Modern cloud-based Internet of Things (IoT) infrastructures face increasingly sophisticated and diverse cyber threats that challenge traditional detection systems in terms of scalability, adaptability, and explainability. In this paper, we present (H-DIR)<sup>2</sup>, a hybrid entropy-ba...

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Bibliographic Details
Main Authors: Davide Tosi, Roberto Pazzi
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Sensors
Subjects:
hybrid distributed information retrieval
entropy-based anomaly detection
associated random neural network (ARNN)
RDF/SPARQL explainability
cloud–IoT security
sub-second detection latency
Online Access:https://www.mdpi.com/1424-8220/25/15/4841
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