Multi-Task Learning for Security and Performance Optimization in 6G Network Slices
DOI:
https://doi.org/10.65204/djes.v3i3.823Keywords:
Multi-Task Learning 6G Network Deep Learning Cybersecurity Throughput Prediction Network CongestionAbstract
As such, the fast-paced move towards sixth-generation (6G) networks presents numerous obstacles in ensuring the attainment of both secure operation and optimized performance within the framework of network slicing systems. Current methods usually handle the two goals separately, leading to less-than-optimal performance in highly volatile and sophisticated network settings. In this study, an innovative multi-task (MLT) algorithm was formulated for the simultaneous forecasting of the security status, throughput, and congestion status of 6G network slices. The new model employs common features extracted from network status, traffic status, and cyberattacks to model the inherent connection between security risks and performance deterioration. The experimental outcomes clearly show that the suggested MTL architecture exhibits superior performance compared to traditional single-task models in terms of accuracy, generalization capacity, and stability.
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