DEEP LEARNING-DRIVEN NETWORK SECURITY THREAT IDENTIFICATION AND MITIGATION

Abstract

The rapid evolution of cyber threats has increased the demand for intelligent and efficient approaches to network security. Deep learning has gained considerable attention in this context because of its ability to learn complex patterns and automatically extract relevant features from large and diverse cybersecurity data. The present paper examines the application of deep learning for network security threat identification and mitigation, with emphasis on the capabilities of different deep learning architectures and their suitability for cybersecurity tasks. CNN, DBN, RBM, Autoencoder, Stacked Autoencoder, RNN, LSTM, and GAN-based approaches are examined in relation to malware detection, intrusion detection, phishing and spam detection, botnet detection, and ransomware detection. The paper further considers recent developments in hybrid deep learning models, GAN-based approaches, federated learning, attention-based techniques, and Transformer-based methods for improving threat detection. Along with these developments, important challenges involving parameter optimization, evaluation metrics, datasets, model architecture, inference, and interpretability are considered. The analysis highlights the growing role of deep learning in developing automated and adaptive security mechanisms and emphasizes the need for reliable datasets, suitable model selection, improved generalization, and explainable approaches for effective cybersecurity applications.