Blockchain and Deep Learning for Secure IoT: A Hybrid Cryptographic Approach

Abstract

The Internet of Things (IoT) has revolutionized device connectivity, but its rapid expansion has raised significant security concerns related to data privacy, device integrity, and unauthorized access. This study explores a hybrid cryptographic approach leveraging blockchain technology and deep learning to enhance IoT security mechanisms. Blockchain provides a decentralized and immutable framework for securing data transactions, while deep learning algorithms can adaptively detect and neutralize attacks by analyzing large datasets. Our proposed method integrates smart contracts for enforcing access controls, ensuring only authorized devices interact within the network. Furthermore, advanced deep learning techniques enable real-time anomaly detection, identifying potential breaches and malicious activities effectively. The research addresses scalability challenges, computational efficiency, and energy consumption, offering tailored solutions suitable for resource-constrained IoT devices. By synthesizing blockchain and deep learning, this approach not only fortifies data integrity and confidentiality but also enhances user trust in IoT systems. Key findings indicate that employing this hybrid model can significantly reduce the incidence of cyberattacks and provide robust security solutions for diverse IoT applications, ranging from smart homes to healthcare and industrial automation. This study aims to establish a foundational framework for future research in secure IoT architectures, paving the way for broader implementation in real-world scenarios.