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

【Author】 Dhanushkodi, Kavitha; Venkataramani, Kiruthika; Pranav, K. R. Naghul; Sethuraman, Ravikumar

【Source】TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES

【影响因子】3.310

【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.

【Keywords】Adam optimization; bidirectional long short term memory; blockchain; cyberattack detection; end-to-end encryption; Gaussian artificial hummingbird optimization; smart contract

【发表时间】2025 JAN

【收录时间】2025-02-16

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