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

Abstract

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.