Explainable artificial intelligence-based cyber resilience in internet of things networks using hybrid deep learning with improved chimp optimization algorithm

The rapid growth of the Internet of Things (IoT) has driven new research into artificial intelligence (AI)-based methods for detecting anomalies. With its advanced capabilities, AI can automate tasks, analyze large datasets, and accurately identify vulnerabilities. The lack of transparency in cybers...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 33160 - 24
Hauptverfasser: Alzakari, Sarah A., Aljebreen, Mohammed, Ahmad, Nazir, Alahmari, Sultan, Alrusaini, Othman, Alqazzaz, Ali, Alkhiri, Hassan, Said, Yahia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 26.09.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705/117
639/705/258
Accuracy
Algorithms
Artificial intelligence
Classification
Curricula
Cybersecurity
Data normalization
Datasets
Decision making
Deep learning
Dimensionality reduction
Efficiency
Explainable artificial intelligence
Health care
Humanities and Social Sciences
Internet of Things
Machine learning
Methods
multidisciplinary
Neural networks
Optimization algorithms
Science
Science (multidisciplinary)
Smart cities
Threats
Deep learning
Dimensionality reduction
Explainable artificial intelligence
Internet of things
Cybersecurity
Data normalization
ISSN:2045-2322, 2045-2322
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Zusammenfassung:The rapid growth of the Internet of Things (IoT) has driven new research into artificial intelligence (AI)-based methods for detecting anomalies. With its advanced capabilities, AI can automate tasks, analyze large datasets, and accurately identify vulnerabilities. The lack of transparency in cybersecurity systems makes it difficult to explain critical decisions and associated risks clearly. Machine learning (ML)-based intrusion detection systems (IDS) excel in threat detection but encounter threats due to limited transparency and scarce attack data, specifically in IoT. This paper presents the Explainable Artificial Intelligence for Cyber Resilience Using a Hybrid Deep Learning and Optimization Algorithm (XAICR-HDLOA) approach to improve cyber threat detection and interpretation in IoT environments. Min-max normalization is initially applied to standardize feature scales, followed by the Bald Eagle Search (BES) model for selecting key features. Moreover, the hybrid Convolutional Neural Networks-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model is employed for cyberattack classification. Furthermore, the Improved Chimp Optimizer Algorithm (IChoA) is implemented for the hyperparameter tuning process. Finally, SHAP is applied to improve model interpretability, increasing trust and reliability in cybersecurity. Simulations on the Edge-IIoT and BoT-IoT datasets highlight the efficiency of the XAICR-HDLOA approach, achieving high accuracy of 98.41% and 98.25%, outperforming existing methods.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-15146-x