Integrating Big Data and AI for Network Security in 6G to Enhance University Financial Management

Abstract

In the 6G network structures, the integration of Big Data (BD) and artificial intelligence (AI) is beneficial for the purpose of improving cybersecurity in university financial management systems. So, the integration of the BD and AI in the 6G structures are suggested in this study. Then, the conventional centralized security systems are ineffective in the rapid digitalization of financial transactions. Because these conventional systems are susceptible to single points of failure (SPF), delayed threat detection, and data privacy breaches. In the real-time (RT) financial backgrounds, these conventional systems face difficulties in protecting the network against advanced cyber threats. These situations will call for a decentralized, adaptive, and privacy-preserving (PP) security framework in the rapidly evolving 6G structures. This demanded framework may help in anomaly detection (AD) in financial transactions without affecting vital data. Thus, a novel federated learning (FL)-based AD in financial security (FL-AD-FS) framework is suggested in this study. To train the AI models collaboratively over several edge devices, this suggested model utilizes FL. This application will also ensure the privacy of the data. Then, in financial operations, the RT AD and threat mitigation was facilitated by the system, as it integrates with 6G-enabled (EC) edge computing. The simulations were conducted; from the outcomes, it is clear that the suggested FL-AD-FS model executes better by reducing false positive rates (FPRs), increasing detection (ACC) accuracy, and minimizing latency. In university backgrounds, secure, fast, and reliable monitoring of financial transactions was facilitated by this suggested method. For revolutionizing cybersecurity in digital financial systems, the potential of the integration of the FL, AI, and 6G technologies is demonstrated by the FL-AD-FS framework. For modern university financial management, this suggested method creates a customized scalable, secure, and privacy-aware solution.