## 1 Introduction  
As the digital landscape evolves, the complexity and frequency of cybersecurity threats increase, presenting substantial challenges for conventional defense mechanisms [1]. In this rapidly changing environment, the variety of threats expands swiftly, creating an ongoing arms race between attackers and defenders. Modern cybersecurity systems must address a wide range of sophisticated threats, including advanced phishing schemes, ransomware attacks, and stealthy advanced persistent threats that seek to remain undetected within networks for extended periods [2]. These circumstances necessitate defense systems that are not only reactive but also adaptive and resilient, with the ability to proactively detect and counteract threats across multiple attack vectors. In response to these challenges, Multi-Layer Perceptron (MLP) neural networks have become a foundational technology in the field of cybersecurity [3]. Renowned for their capacity to learn and model the complex, nonlinear relationships present in large-scale data, such as network traffic, MLPs signify a transformative shift in the detection and response to cyber threats. As a type of feedforward artificial neural network, MLPs are particularly effective in pattern recognition tasks, which are essential for identifying anomalous behavior indicative of cyber threats. By analyzing extensive datasets, MLPs can reveal subtle patterns that are often missed by traditional rule-based systems, providing a more adaptive and robust approach to threat detection [4]. MLPs consist of multiple layers of neurons, with each layer designed to capture different levels of data abstraction. This structure allows them to detect not only simple patterns but also complex interactions within the data, making them particularly well suited for the intricate task of threat detection in cybersecurity [5]. However, the effectiveness of MLPs is highly dependent on the optimal configuration of their architecture, particularly the tuning of their weights and biases. These parameters are systematically adjusted during training to minimize prediction errors. Achieving this tuning is a non-trivial task, as it involves navigating a high-dimensional parameter space often plagued by local minima, which can hinder conventional gradient-based optimization methods [6]. Moreover, training MLPs necessitates advanced optimization techniques capable of effectively balancing the exploration of the parameter space with the exploitation of promising regions [7]. Traditional methods, such as backpropagation and its variants, frequently encounter difficulties, especially in complex scenarios involving noisy, imbalanced, or incomplete datasets, which are common in cybersecurity applications [8; 9]. These shortcomings have driven the development of more sophisticated optimization algorithms that offer the robustness and flexibility required to train MLPs efficiently under such challenging conditions. Metaheuristic algorithms and artificial intelligence (AI) have revolutionized the field of optimization, enabling the solution of complex problems that have traditionally presented significant difficulties for conventional methods [10]. These advanced techniques leverage natural processes and intelligent algorithms to navigate vast and intricate search spaces, efficiently identifying near-optimal solutions that may be unattainable through standard approaches. Inspired by a range of natural phenomena, including biological evolution, social behaviors of organisms, and physical processes, metaheuristics provide a flexible and dynamic framework for addressing complex optimization problems [11]. This is particularly relevant in the field of cybersecurity, where the adaptive and robust characteristics of metaheuristics can substantially improve the performance of systems such as Multi-Layer Perceptrons (MLPs) in detecting and responding to cyber threats. AI techniques, particularly machine learning (ML) and deep learning (DL), have significantly enhanced the capabilities of metaheuristic algorithms [12].