The Impact of Artificial Neural Network Architecture on Network Attack Detection
Abstract
Artificial Neural Networks (ANNs) have emerged as a powerful tool for network attack detection due to their ability to learn complex patterns and behaviors. The architecture of an ANN plays a critical role in determining its performance and effectiveness in detecting network attacks. This article explores the impact of ANN architecture on network attack detection, highlighting different types of architectures used in the field of cybersecurity. It discusses the advantages and disadvantages of these architectures, along with the challenges associated with their usage. By understanding the significance of ANN architecture, security professionals can make informed decisions to enhance network defense mechanisms and protect against evolving cyber threats.