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Related Experiment Videos

DeepSense: An Adaptive Scalable Ensemble Framework for Industrial IoT Anomaly Detection.

Amir Firouzi1, Ali A Ghorbani1

  • 1Faculty of Computer Science, University of New Brunswick (UNB), Fredericton, NB E3B 5A3, Canada.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

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CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment.

Sensors (Basel, Switzerland)·2023
See all related articles

DeepSense offers a hybrid anomaly detection system for Industrial Internet of Things (IIoT) security. This adaptive framework enhances Industrial Internet of Things security by combining rule-based and machine learning approaches for robust intrusion detection.

Area of Science:

  • Cyber-Physical Systems Security
  • Industrial Automation and Control
  • Machine Learning for Security

Background:

  • Industrial Internet of Things (IIoT) environments face increasing security challenges due to scale, heterogeneity, and dynamic behavior.
  • Conventional security mechanisms struggle to cope with the expanded attack surface in modern IIoT deployments.
  • Resource-constrained and heterogeneous IIoT systems require specialized security solutions.

Purpose of the Study:

  • To propose DeepSense, a hybrid and adaptive anomaly and intrusion detection framework for IIoT.
  • To develop a realistic data pipeline and experimental testbed (DataSense) for synchronized sensor and network data processing.
  • To create a comprehensive evaluation framework assessing detection quality, latency, resource efficiency, and coverage.

Main Methods:

Keywords:
IIoT securityIndustrial Internet of Things (IIoT)Internet of Things (IoT)adaptive ensemble learninganomaly detectioncyber–physical systems securitydeep learningintrusion detection system (IDS)machine learning

Related Experiment Videos

  • DeepSense integrates three components: DataSense (data pipeline/testbed), RuleSense (edge-based rule detection), and NeuroSense (adaptive ML/DL ensemble).
  • NeuroSense utilizes an ensemble of 22 models (classical, neural, hybrid, Transformer) for validating suspicious events and classifying attacks.
  • A Pareto-optimal ensemble selection process is employed under realistic IIoT constraints.

Main Results:

  • DeepSense demonstrated strong generalization capabilities across diverse detection scenarios.
  • The framework achieved lower false positive rates and robust performance against evolving attack behaviors.
  • Experimental results validated the scalability and efficiency of DeepSense for IIoT security.

Conclusions:

  • DeepSense provides a scalable and efficient security solution tailored for resource-constrained and heterogeneous IIoT environments.
  • The framework meets the operational demands of Industry 4.0 and the resilience goals of Industry 5.0.
  • DeepSense offers a promising approach to enhance the security posture of industrial cyber-physical systems.