<![CDATA[The rapid development of information technology has significantly increased the complexity of computer network infrastructures. Along with these developments, cyber threats such as malware attacks have also increased in frequency and sophistication. Malware attacks can cause serious damage to computer systems, including data breaches, service disruption, and financial losses. Therefore, early detection and prediction mechanisms are crucial to enhance network security systems. Machine Learning has emerged as an effective approach for detecting and predicting cyber threats by analyzing large-scale network traffic data and identifying abnormal patterns. This study aims to develop a machine learning-based model for predicting malware attacks in computer network systems. Several machine learning algorithms such as Random Forest, Support Vector Machine, and Decision Tree are evaluated to determine the most effective model for malware prediction. The proposed model analyzes network traffic features and classifies them into normal or malicious behavior using supervised learning techniques. The experimental results demonstrate that machine learning models can significantly improve the accuracy of malware attack prediction and provide an efficient mechanism for proactive network security defense. This research contributes to the development of intelligent cybersecurity systems capable of detecting and predicting malware threats in modern computer networks.]]>
Copyrights © 2025
