Paper Title
DEEP LEARNING TECHNIQUES FOR CYBERSECURITY: A COMPREHENSIVE SURVEY WITH FOCUS ON DATABASE APPLICATIONS

Abstract
As cyber threats targeting database systems grow more sophisticated, traditional security methods struggle to keep pace. Deep Learning (DL) provides a data-driven framework for detecting anomalies, predicting attacks, and supporting adaptive cybersecurity strategies. This survey examines recent DL techniques applied to cybersecurity, particularly those emphasizing database-related tasks. We review key architectures—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Generative Adversarial Networks (GANs), and Graph Neural Networks (GNNs)—and analyze their effectiveness, limitations, and suitability for database integration. The paper also presents a classification of approaches, comparative evaluation, and identifies open research challenges, offering guidance for future work in developing secure, intelligent database systems. Keywords - Cybersecurity, Deep Learning, Database Security, Intrusion Detection, Neural Networks, Autoencoders, GANs