NeuroSecure: A Comprehensive Survey on Deep Learning Approaches for Cyber Defense and Intrusion Detection

Abstract: Cyber threats have grown dramatically in both scale and sophistication, outpacing the detection capabilities of classical signature-based and rule-driven security tools. This paper surveys the evolution of Intrusion Detection Systems (IDS) from their early reliance on static pattern matching through to modern deep learning–driven architectures, drawing on peer-reviewed publications and benchmark evaluation studies. We review work spanning core algorithmic approaches—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer architectures, and ensemble methods—alongside their application to network traffic analysis using the NSL-KDD and CICIDS2017 benchmark datasets. To bring structure to this body of literature, we introduce a four-tier classification framework organized around increasing system sophistication: from basic signature matching through anomaly detection and hybrid approaches, to fully integrated, context-aware deep learning platforms. We then present a hybrid ensemble IDS framework that combines CNN, RNN, and Transformer models through a majority-voting fusion layer, achieving detection accuracy exceeding 96% with precision, recall, and F1-score consistently above 94% across all attack categories. Performance dimensions examined include classification accuracy, precision-recall balance, generalization to unseen threats, and scalability for enterprise and cloud environments. A recurring observation across reviewed studies is the absence of any single system that simultaneously handles diverse attack types, class imbalance, feature redundancy, and real-time traffic volumes within one coherent architecture. We discuss the practical implications of this gap and outline directions for future research.

Keywords: Intrusion Detection Systems; Deep Learning; Convolutional Neural Networks; Recurrent Neural Networks; Transformer Models; Network Security; NSL-KDD; CICIDS2017; Ensemble Learning; Cybersecurity; Anomaly Detection; Zero-Day Attacks.