Role of GenAI tools in real-time 5G network forensics for hybrid clouds

Background: The deployment of the fifth-generation (5G) networks and hybrid clouds has completely reshaped the world of telecommunication, bringing new dilemmas in network security, forensic examination, and real-time threat recognition never seen before. This is an extensive research study that explored the significance of the Generative Artificial Intelligence (GenAI) tools in improving real-time 5G network forensics in the hybrid cloud infrastructures. In their research they have used both qualitative and quantitative methods of research, combining both quantitative methods using structured questionnaires that were given to 450 cybersecurity experts and qualitative techniques that included 45 semi-structured expert interviews conducted in various geographical locations. Secondary data was collected by a systematic review of 2,847 published network forensic incidents reported between January 2023 and December 2024, and was complemented with experimental testing of six of the most popular GenAI-based forensic platforms executed in a controlled 5G testbed setting.

Methodology: Statistical analysis using SPSS version 28.0 showed that implementation of GenAI had significant correlations with forensic efficiency measures with multiple regression analysis showing that GenAI tools had a significant contribution to reduction of variance in incident response times (R² = 0.673, p <0.001) The results of the research proved that GenAI-enhanced forensic systems had 89.4 percent accuracy in detecting anomalies as opposed to 62.7 percent accuracy in the traditional rule-based systems. Moreover, the chi-square tests (2 = 142.56, 8, p < 0.001)  revealed that the Improved hreat classification accuracy across hybrid cloud architectures was statistically significant between GenAI deployment and the use of the hybrid cloud architecture. The hybrid deep learning-federated learning models were able to better perform distributed forensic analysis with the false positive rates going as low as 8.2% compared to 34.6% before ensuring at the same time the data sovereignty requirements across the cloud providers. 

Results: Results showed that the explainable artificial intelligence (XAI) implementation in the GenAI forensic tools contributed to the improvement of transparency in the automated decision-making system, which is a critical issue in terms of the accountability of algorithms in judicial work. Nevertheless, there remained issues of adversarial attacks on GenAI models, computational resource needs to achieve real-time processing and regulatory complexity of complying with multiple international jurisdictions. The study formulated four overall research objectives investigating the efficacy of GenAI, difficulties in implementation, measures of comparative performance and future integration plans. Three null hypotheses were put to test and rejected in which the relationships between GenAI and forensic capabilities were positive, significant differences existed between GenAI and conventional systems and that XAI implementation related to forensic reliability.

Discussion and Conclusion: This scholarly work has brought new knowledge into the overlap of generative artificial intelligence, telecommunication security and digital forensics and presented empirical evidence on the importance of GenAI as a strategic component in the next generation network security systems. The paper provides practical suggestions to cybersecurity professionals, telecommunications organizations, cloud computing organizations, and regulatory authorities that aim to use GenAI to scale up network forensic activities in more complex 5G hybrid clouds.