Utilizing Artificial Intelligence in Cybersecurity: A Study of Neural Networks and Support Vector Machines

Abstract

The rapid evolution of network security threats necessitates the integration of advanced technologies, particularly artificial intelligence (AI), in the development of effective protection systems. This article reviews contemporary methodologies employing AI for enhancing computer network security, with a focus on neural networks (NN) and support vector machines (SVM). It begins by elucidating the architecture of neural networks, including the training and recognition phases essential for detecting malicious activities within a network. The effectiveness of NN in identifying patterns indicative of unauthorized access is highlighted, alongside the challenges associated with training datasets. Further, the article explores the application of SVM in classifying network traffic and detecting unwanted software through geometric interpretations of classification tasks. It also emphasizes the growing trend of AI technology in modern antivirus utilities and network security analysis programs, advocating for the integration of multi-layered protective measures that leverage AI's learning capabilities. Finally, the potential of AI methodologies to unveil new pathways for research and application in network security is discussed, underscoring the need for continued exploration of these promising technologies to safeguard digital infrastructures against evolving threats.