Comprehensive Deep Learning Methodologies for the Detection and Mitigation of Zero-Day Attacks within Large-Scale Heterogeneous Network Environments

Timothy Edward Allen

Authors

Timothy Edward Allen Cybersecurity Data Scientist, United States. Author

Keywords:

Zero-day attacks, deep learning, cybersecurity, heterogeneous networks, anomaly detection, CNN, RNN, Transformer, intrusion detection systems, network security

Abstract

Purpose

The primary purpose of this paper is to explore, implement, and evaluate deep learning (DL) methodologies tailored for the detection and mitigation of zero-day attacks within expansive and heterogeneous network environments. Zero-day attacks pose a critical challenge due to their unknown signatures and rapid propagation. This study aims to address these threats using scalable, adaptive, and high-accuracy DL techniques.

Design/methodology/approach

A multi-model DL architecture is proposed, integrating Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based attention mechanisms. A diverse dataset was synthesized using emulated network environments combined with public threat datasets to simulate large-scale heterogeneous network conditions. Evaluation metrics include detection accuracy, false positive rate, and latency of response.

Findings

The hybrid deep learning system demonstrated superior performance in anomaly detection, especially in identifying novel attack patterns typical of zero-day exploits. CNN-RNN fusion improved spatial-temporal detection granularity, while Transformer layers enhanced model generalizability across varied network topologies.

Practical implications

Findings can be applied to security operations centers (SOCs), cloud service infrastructures, and critical information systems, enabling near real-time threat detection and automated mitigation of emerging vulnerabilities in network systems.

Originality/value

This work presents a unified and scalable DL framework explicitly designed for real-time detection and response to zero-day attacks across complex and heterogeneous digital infrastructures. Its novelty lies in the integration of attention mechanisms with classical DL models to enhance threat detection under high traffic and polymorphic attack conditions.

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