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

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  • 1Department of Theoretical and Applied Sciences, Università degli Studi dell'Insubria, 21100 Varese, Italy.

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This study introduces a hybrid framework, (H-DIR)² (Hybrid Detection and Response), for advanced cloud-IoT cybersecurity. It effectively detects and mitigates network anomalies with high accuracy and low latency.

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RDF/SPARQL explainabilityassociated random neural network (ARNN)cloud–IoT securityentropy-based anomaly detectionhybrid distributed information retrievalsemantic adaptive cyber defensesub-second detection latency

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Area of Science:

  • Cybersecurity
  • Network Anomaly Detection
  • Internet of Things (IoT)

Background:

  • Cloud-based IoT infrastructures face evolving cyber threats challenging traditional security.
  • Existing systems struggle with scalability, adaptability, and explainability in detecting sophisticated attacks.

Purpose of the Study:

  • To present (H-DIR)² a hybrid framework for detecting and mitigating anomalies in large-scale heterogeneous cloud-IoT networks.
  • To enhance cybersecurity by combining entropy analysis, neural networks, and semantic reasoning.

Main Methods:

  • Implemented a hybrid entropy-based framework, (H-DIR)², integrating Shannon entropy, Associated Random Neural Networks (ARNNs), and RDF/SPARQL semantic reasoning.
  • Utilized a distributed Apache Spark 3.5.0 pipeline for processing and analysis.
  • Validated the framework on real-world datasets against SYN Flood, DAO-DIO, and NTP amplification attacks.

Main Results:

  • Achieved a mean detection latency of 247 ms and an AUC of 0.978 for SYN floods.
  • Improved packet delivery ratio from 81.2% to 96.4% for DAO-DIO manipulations (p < 0.01).
  • Reduced peak load by 88% during NTP amplification attacks, demonstrating significant mitigation capabilities.

Conclusions:

  • The (H-DIR)² framework offers a transparent, scalable, and reproducible solution for cloud-IoT cybersecurity.
  • It establishes a robust baseline for detecting diverse threats, including edge-aware and zero-day attacks.
  • The system demonstrates effective vertical and horizontal scalability for massive datasets and endpoint networks.