A Review of Deep Learning Techniques for Optimizing Accuracy in Network Attack Detection
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Abstract
As cyber threats grow in complexity and volume, traditional network security approaches are increasingly unable to provide effective and timely protection. Deep learning has emerged as a transformative solution for detecting network attacks with improved precision and adaptability. This review presents a comprehensive analysis of current deep learning techniques applied to network intrusion detection systems (NIDS). It explores how models like CNNs, RNNs, LSTMs, and Autoencoders can learn from vast, high-dimensional network data to detect both known and unknown threats. The study also examines hybrid architectures and ensemble models that enhance detection accuracy by integrating multiple learning methods. Emphasis is placed on the role of data preprocessing, feature selection, and evaluation metrics in optimizing model performance. Moreover, key challenges such as limited labeled data, real-time detection constraints, interpretability issues, and resilience against adversarial attacks are critically discussed. The review concludes by highlighting emerging trends, including the use of federated learning, lightweight DL models for edge deployment, and explainable AI for transparent threat analysis. Overall, the paper provides valuable insights into how deep learning continues to shape the future of intelligent and efficient network attack detection systems.
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