OPTIMIZING NEURAL NETWORK ARCHITECTURE FOR ENHANCED ATTACK DETECTION: A COMPREHENSIVE APPROACH

In the realm of cybersecurity, robust attack detection mechanisms are imperative due to the increasing sophistication of cyber threats. Machine learning techniques, particularly neural networks, have emerged as powerful tools for identifying and mitigating these attacks. The performance of a neural network heavily relies on its architecture, including features, hidden layers, and hidden neurons. This article explores the intricacies of optimizing neural network architecture to enhance attack detection, drawing from recent research and practical applications. The significance of feature selection, hidden layers, and hidden neurons is examined in various attack detection contexts. Additionally, methods for determining the count of features and choosing appropriate hidden layers and neurons are investigated, considering criteria such as dataset size, domain knowledge, and regularization techniques. The article underscores the pivotal role that neural network architecture plays in achieving accurate and efficient attack detection.