Mobile Network Data Security using Deep Learning

With the rapid expansion of mobile networks and the exponential growth of data transmission, ensuring robust security against cyber threats has become a critical challenge. Traditional security mechanisms struggle to keep pace with evolving attack strategies, necessitating intelligent and adaptive solutions. This research explores the application of deep learning techniques to enhance mobile network data security by detecting anomalies, preventing intrusions, and securing communication channels. The study leverages advanced neural network architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models, to analyze traffic patterns and identify potential threats in real time. A hybrid deep learning framework is proposed to optimize threat detection efficiency while minimizing false positives. Experimental evaluations on real-world datasets demonstrate significant improvements in accuracy, speed, and resilience against sophisticated cyberattacks compared to conventional security methods. The findings highlight the potential of deep learning-based approaches in strengthening mobile network security, ensuring data integrity, and safeguarding user privacy in an increasingly interconnected digital landscape.