Adversarial Machine (Deep) Learning-basedRobustification in 5G Networks

Abstract:  A significant development in wireless communication and artificial intelligence has been made possible by the combination of 5G networks with deep learning methods. This paper explores the complex interactions between these areas, concentrating on the dangers that adversarial attacks represent in the context of 5G network slicing. Multiclass classification models are created first, utilizing CNN, LSTM, and MLP architectures using a thorough three-phase process. Real adversarial attacks like FGSM, CW, BIM, and PGD are subsequently created to highlight the models' vulnerability to manipulation. The result highlights the need for strong protection measures by highlighting the upsetting potential of these attacks. The recommended defensive methods are addressed in the last stage, providing potential countermeasures to adversary threats. This study emphasizes the significance of taking into account ecological and societal implications while accepting such breakthroughs by bridging the technology and sustainability components. Integrating sustainability into the conversation becomes increasingly important as we advance the boundaries of technological innovation. By doing this, it is provided the foundation for a future that balances technical advancement with ethical progress, promoting a more robust and inclusive digital environment.