BGHO-E2EB Model: Enhancing IoT Security With Gaussian Artificial Hummingbird Optimization and Blockchain Technology

Abstract

The Internet of Things (IoT) is transforming numerous sectors but also presents unique security challenges due to its interconnected and resource-constrained devices. This study introduces the Bidirectional Gaussian Hummingbird Optimized End-to-End Blockchain (BGHO-E2EB) model, designed to detect and classify cyberattacks within IoT environments. Unlike preventive approaches, the developed model focuses on real-time detection and categorization of attacks, enabling timely responses to emerging threats. The proposed model integrates blockchain technology through Ethereum-based smart contracts to enhance the security and integrity of data exchanges within IoT networks. Additionally, a Gaussian Artificial Hummingbird Algorithm is employed for optimal feature selection, minimizing data dimensionality and computational load. A Bidirectional Long Short-Term Memory (Bi-LSTM) network further improves the model's capability by accurately detecting and categorizing cyber threats based on selected features. The Adam optimizer is used for efficient parameter tuning within the Bi-LSTM network, ensuring high-performance cyberattack detection. The proposed model was evaluated using established IoT security benchmarks, including the UNSW-NB15, BOT-IoT, and NSL-KDD datasets, accomplishing an accuracy of 98.7%, precision of 96.3%, and security level of 99.5%, significantly outperforming traditional methods. These results demonstrate the effectiveness of BGHO-E2EB as a robust tool for detecting and classifying cyberattacks in IoT networks, making it suitable for real-world deployment in dynamic IoT environments where security is paramount.