The rapid expansion of the Industrial Internet of Things (IIoT) has transformed modern industrial environments by enabling intelligent automation, real-time monitoring, and large-scale connectivity among devices. However, the increasing number of interconnected and resource-constrained devices exposes industrial networks to significant cybersecurity threats such as unauthorized access, data manipulation, and distributed denial-of-service attacks. To address these challenges, researchers have explored various security mechanisms including lightweight cryptography, blockchain technology, and artificial intelligence–based intrusion detection systems. Elliptic Curve Cryptography (ECC) has been widely adopted for secure and efficient encryption in IoT environments due to its lower computational overhead. Blockchain technology provides decentralized and tamper-resistant data management, improving trust and transparency among distributed nodes. In addition, machine learning and federated learning approaches have been increasingly applied for intelligent threat detection in IoT networks. This paper presents a comprehensive survey of existing security frameworks that integrate ECC, blockchain, and AI-based intrusion detection techniques for Industrial IoT systems. The study analyzes recent research contributions, compares different approaches, and identifies key limitations and research gaps in current IIoT security solutions. Finally, future research directions are highlighted to guide the development of scalable, secure, and intelligent Industrial IoT architectures.
B.Kavipriya et al. (Wed,) studied this question.