Deep Learning Based Data Security in Mobile Networks

Due to their growing importance, wireless communication networks are more vulnerable to malicious assaults, particularly at the physical layer where protections are more lax. An effective method for making these systems more resistant to jamming, eavesdropping, and spoofing assaults, among others, is deep learning (DL). In this research, we look at how deep learning may be used to strengthen wireless communication systems' physical-layer security. We present a system that uses deep learning to identify, stop, and prevent malicious assaults as they happen. In order to make the framework more resistant to attacks, it uses adversarial training techniques, which include creating defensive tactics using generative adversarial networks (GANs) and detecting attacks with convolutional and recurrent neural networks (RNNs).