Abstract

Deep neural networks (DNNs) have become pivotal in modern cybersecurity due to their capacity to learn complex patterns and detect sophisticated cyber threats. Traditional signature-based detection systems struggle to identify zero-day attacks, polymorphic malware, and advanced persistent threats (APTs) because these methods lack adaptability and deep pattern recognition. This paper explores advanced deep neural network architectures—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Autoencoders, Generative Adversarial Networks (GANs), and hybrid deep models—for cyber attack identification. We survey foundational and contemporary research on the application of these architectures to intrusion detection systems (IDS), malware classification, anomaly detection, and network traffic analysis. A detailed methodology outlines dataset selection, preprocessing, model training, evaluation metrics, and performance comparison. We analyze advantages such as adaptive learning, high detection accuracy, and feature extraction capabilities alongside disadvantages including computational complexity, training data requirements, and interpretability challenges. Results indicate that hybrid models combining spatial and temporal feature learning deliver superior detection performance, while autoencoder-based anomaly detection outperforms traditional machine learning in high-dimensional data scenarios. The paper concludes with insights on practical implementation, current limitations, and future research directions to enhance robustness, scalability, and real-time responsiveness of DNN-based cyber attack identification systems