The detection of computer network attacks is becoming increasingly important as the complexity of cyber-attacks threatening information systems and infrastructure continues to rise. To address these threats, artificial intelligence techniques have become a primary focus in the development of more effective attack detection systems. One algorithm that has proven reliable in this context is the C4.5 decision tree. This study aims to apply the C4.5 algorithm in network attack detection using a dataset that includes various types of attacks and network activities. The process includes data preprocessing, decision tree model building, and model performance evaluation. The results show that the C4.5 decision tree algorithm is effective in classifying network activities into attacks and normal activities with a satisfactory level of accuracy. The model successfully recognizes attack-related patterns, and further analysis identifies key factors influencing attack detection. This research provides a significant contribution to the development of reliable and efficient attack detection systems in computer networks. By applying the C4.5 decision tree algorithm, it is expected to help enhance information security and protect network infrastructure from increasingly complex cyber threats
