Detection of cyber attack in network using hybrid decision tree technique

Because attackers have the potential to seriously harm systems and networks, cyber attacks have grown to be a serious problem for network security. In this study, we provide a hybrid decision tree method for network traffic cyber attack detection. To find abnormalities in network data, our method combines the benefits of machine learning algorithms with decision trees. A dataset of labeled network traffic is used to assess the proposed system, and it is found to be successful in identifying a variety of cyber attacks, such as denial-of-service (DoS) and unauthorized access attempts.