Protecting Supervisory Control and Data Acquisition-Industrial Internet of Things (SCADA-IIoT) systems against intruders has become essential since industrial control systems now oversee critical infrastructure, and cyber attackers more frequently target these systems. Due to their connection of physical assets with digital networks, SCADA-IIoT systems face substantial risks from multiple attack types, including Distributed Denial of Service (DDoS), spoofing, and more advanced intrusion methods. Previous research in this field faces challenges due to insufficient solutions, as current intrusion detection systems lack the necessary accuracy, scalability, and adaptability needed for IIoT environments. This paper introduces CyberFortis, a novel cybersecurity framework aimed at detecting and preventing cyber threats in SCADA-IIoT systems. CyberFortis presents two key innovations: Firstly, Siamese Double Deep Q-Network with Autoencoders (Siamdqn-AE) FusionNet, which enhances intrusion detection by combining deep Q-Networks with autoencoders for improved attack detection and feature extraction; and secondly, the PopHydra Optimiser, an innovative solution to compute reinforcement learning discount factors for better model performance and convergence. This method combines Siamese deep Q-Networks with autoencoders to create a system that can detect different types of attacks more effectively and adapt to new challenges. CyberFortis is better than current top attack detection systems, showing higher scores in important areas like accuracy, precision, recall, and F1-score, based on data from CICIoT 2023, UNSW-NB 15, and WUSTL-IIoT datasets. Results from the proposed framework show a 97. 5% accuracy rate, indicating its potential as an effective solution for SCADA-IIoT cybersecurity against emerging threats. The research confirms that the proposed security and resilience methods are successful in protecting vital industrial control systems within their operational environments.

Blockchain Technology (BT) has emerged as a transformative solution for improving the efficacy, security, and transparency of supply chain intelligence. Traditional Supply Chain Management (SCM) systems frequently have problems such as data silos, a lack of visibility in real time, fraudulent activities, and inefficiencies in tracking and traceability. Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues; it facilitates trust, security, and the sharing of data in real-time among all parties involved. Through an examination of critical technologies, methodology, and applications, this paper delves deeply into computer modeling based-blockchain framework within supply chain intelligence. The effect of BT on SCM is evaluated by reviewing current research and practical applications in the field. As part of the process, we delved through the research on blockchain-based supply chain models, smart contracts, Decentralized Applications (DApps), and how they connect to other cutting-edge innovations like Artificial Intelligence (AI) and the Internet of Things (IoT). To quantify blockchain’s performance, the study introduces analytical models for efficiency improvement $(\eta )$, security enhancement $(\delta )$, and scalability $(S_c)$, enabling computational assessment and simulation of supply chain scenarios. These models provide a structured approach to predicting system performance under varying parameters. According to the results, BT increases efficiency by automating transactions using smart contracts, increases security by using cryptographic techniques, and improves transparency in the supply chain by providing immutable records. Regulatory concerns, challenges with interoperability, and scalability all work against broad adoption. To fully automate and intelligently integrate blockchain with AI and the IoT, additional research is needed to address blockchain’s current limitations and realize its potential for supply chain intelligence.