Security

Background: Contemporary multi-protocol networks necessitate scalability, reliability, energy efficiency, and security due to the increasing number of devices and the diversification of network traffic. Conventional network management methods are inadequate to meet these demands, necessitating sophisticated solutions. Artificial intelligence (AI) has emerged as a significant field, offering advanced methods including predictive maintenance, anomaly detection, and intelligent resource management. Objective: This article aims to critically evaluate the effectiveness, flexibility, and productivity of AI-based applications in addressing major challenges in network management, including performance, scalability, energy consumption, threat detection rates, and cost. Methods: The study employs simulations and modeled datasets to assess AI-oriented solutions across various network environments, such as industrial IoT, smart cities, and telecommunications. The evaluation encompasses factors including Mean Time Between Failure (MTBF), resource utilization, delay minimization, and operating cost reduction. Digital twins, intelligent routing algorithms, and self-attention-based anomaly detection models are utilized, and the overall performance of these integrated technologies is analyzed. Results: The analysis demonstrates that AI-powered systems achieve near-optimal performance across all evaluated indicators. Specifically, the Manufacturing and Automotive Knowledge (MAK) sector observed a 52% increase in MTBF, the Banking, Financial Services, and Insurance (BFSI) sector noted a 32.39% improvement in energy efficiency, and the Defense and Public Enterprise (DPE) sector experienced a 94% increase in advanced threat detection. Conclusion: The findings indicate that AI solutions can effectively address many of the challenges present in current networks, offering cost-efficient and secure methods for implementing new communication networks with vast potential. Nonetheless, further empirical research is necessary to generalize these results and validate their applicability in real-world scenarios.

Background: The length and depth of global supply networks have been rising over time, causing permanent problems in visibility, protection, and performance. Blockchain technology has come out as a disruptive technology to tackle all these problems, by providing decentralized, safe and transparent systems.  Objective: This article examines the use of blockchain technology within the context of supply chain and specifically digs deeper into the area of increased transparency, security and subsequent efficient supply chain transactions. The aim is to show that it is possible to transform resource supply chain operations through blockchain and engender trust amongst stakeholders. Methods: Permissioned blockchain system has been design and implemented using proof of Authority (PoA) consensus algorithm. IoT sensors were deployed to obtain data in real-time, and smart contracts were incorporated to perform the tasks of product evaluation, and payment authorization. The performance of the system was assessed depending on the indicators including the number of transactions per time or volume per time, time taken for each transaction, auditability and workability. Results: The combination of high transaction throughput with low latency made the blockchain system scalable as well as operationally stable. Smart contracts were able to minimize the time taken and mistakes, and improve the integration of IoT in relation to tracing transactions in real time. The system also proved that it had the ability to withstand cyber assaults and no data were compromised.  Conclusion: Based on the analysis of supply chain problems, blockchain technology can be used to transform supply chain management. Furthermore, more studies should be done on the future compatibility of the usage of such a system with new technology trends and also its implementation in multiple regions supply chain to harness this valuable system.