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Explainable artificial intelligence-based cyber resilience in internet of things networks using hybrid deep learning

Sarah A Alzakari1, Mohammed Aljebreen2, Nazir Ahmad3

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Scientific Reports
|September 26, 2025
PubMed
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This summary is machine-generated.

This study introduces an Explainable Artificial Intelligence for Cyber Resilience (XAICR) approach using hybrid deep learning and optimization. It significantly enhances cyber threat detection and interpretation in Internet of Things (IoT) environments.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Internet of Things

Background:

  • The proliferation of Internet of Things (IoT) devices necessitates advanced anomaly detection methods.
  • Existing AI and Machine Learning (ML) based Intrusion Detection Systems (IDS) for IoT face challenges with transparency and limited attack data.
  • Lack of transparency in cybersecurity hinders clear explanation of critical decisions and associated risks.

Purpose of the Study:

  • To present an Explainable Artificial Intelligence for Cyber Resilience Using a Hybrid Deep Learning and Optimization Algorithm (XAICR-HDLOA) approach.
  • To enhance cyber threat detection and interpretation specifically within IoT environments.
  • To improve trust and reliability in cybersecurity through enhanced model interpretability.

Main Methods:

Keywords:
CybersecurityData normalizationDeep learningDimensionality reductionExplainable artificial intelligenceInternet of things

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  • Data preprocessing using min-max normalization.
  • Feature selection via the Bald Eagle Search (BES) model.
  • Cyberattack classification using a hybrid Convolutional Neural Networks-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model.
  • Hyperparameter tuning with the Improved Chimp Optimizer Algorithm (IChoA).
  • Model interpretability enhancement using SHAP (SHapley Additive exPlanations).

Main Results:

  • The XAICR-HDLOA approach achieved high accuracy rates of 98.41% on the Edge-IIoT dataset and 98.25% on the BoT-IoT dataset.
  • Demonstrated superior performance compared to existing methods in cyber threat detection and interpretation.
  • Successfully improved model interpretability, fostering greater trust and reliability.

Conclusions:

  • The proposed XAICR-HDLOA method effectively addresses the limitations of current AI/ML-based IDS in IoT.
  • Explainable AI techniques significantly contribute to more transparent and reliable cybersecurity solutions.
  • The approach offers a promising direction for robust cyber resilience in interconnected environments.