HOW CLIMATE CHANGE AFFECTS HUMAN HEALTH

Abstract

This study investigates the application of machine learning algorithms for detecting and preventing cyberattacks. Models including Decision Trees, Support Vector Machines (SVM), Random Forests, and Neural Networks were evaluated using datasets related to network traffic, intrusion detection, and malware classification. Data preprocessing techniques—such as cleaning, feature selection, and class balancing—were employed to enhance model performance. Among the tested models, Neural Networks achieved the highest performance across accuracy, precision, recall, and F1-score metrics, followed by Random Forests. The findings underscore the effectiveness of machine learning in identifying complex attack patterns and enhancing real-time cybersecurity systems.