The Convergence of AI and Blockchain Technologies: A Review on Enhancing IoT Security
Zina Balani, Maryam Mohammed · 2025
The fast expansion of the Internet of Things (IoT), especially in the Industrial Internet of Things (IIoT), has brought significant security challenges, as interconnected devices have become key attacks for cyber threats. Traditional security techniques generally struggle to provide scalable, sufficient preservation. Therefore, this investigation examines the combination of blockchain and artificial intelligence (AI) as a possible solution to boost IoT security. Blockchain, a decentralized ledger technology, provides data integrity, transparency, immutable user authentication, and secure logging of transactions across several systems. Meanwhile, AI facilitates anomaly detection and predictive threat analysis by recognizing unexpected behaviors in network traffic. By utilizing past data, AI can anticipate possible cyberattacks, allowing industries to take preventive security measures. To resolve existing challenges, this investigation proposes the Artificial Intelligence Lightweight Blockchain Security Model (AILBSM), which combines trust management, blockchain-based privacy protection, and AI-driven attack detection throughout a designed three-level security layout. Initially, AILBSM assesses device authenticity to provide the reliability of IIoT sensors. Next, a lightweight blockchain algorithm is used for data authentication and attack prevention. Eventually, an AI-driven mechanism labels cyber threats, leveraging real-time security observation. AILBSM advances current solutions by minimizing processing load with a lightweight blockchain framework and strengthening protection through automated irregularity identification. Unlike standard models, which are power-consuming, AILBSM improves performance without compromising data integrity. This model empowers businesses to enhance security, optimize operations, make better decisions, and handle IoT issues while securing flexible digital safety.