ABSTRACT Deep learning has become foundational in modern cybersecurity solutions, particularly, in intrusion detection, malware analysis, and anomaly detection. However, its effectiveness is often constrained by the challenges of high‐dimensional feature spaces and complex hyperparameter settings. In recent years, nature‐inspired optimization techniques—such as genetic algorithms, particle swarm optimization, ant colony optimization, firefly algorithm, and differential evolution—have been increasingly explored to overcome these limitations. These algorithms offer global search capabilities, flexibility, and robustness, making them well‐suited for optimizing deep learning systems in adversarial and dynamic cyber environments. This article presents a comprehensive review of peer‐reviewed literature published between 2020 and 2024, focusing on integrating nature‐inspired optimization techniques into deep learning for cybersecurity. The review is structured around two core optimization dimensions: (i) feature selection and (ii) hyperparameter tuning. For each, we critically evaluate representative methods, discuss empirical findings across multiple application domains (e.g., IoT, ICS, Android), and highlight how these algorithms address key performance bottlenecks. This review aims to guide researchers in developing robust and adaptive deep learning models for security‐critical applications by synthesizing trends, identifying gaps, and outlining design principles. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Technologies > Machine Learning Technologies > Artificial Intelligence
Mallidi et al. (Mon,) studied this question.