Cyberspace over wireless networks performing network forensics analysis of threats with machine learning methods

With the development of technology, attacks on technological devices have increased. With increasing cyber attacks, the devices we use can be under threat at any time. An attacker's access to any of these devices that are in communication with each other can endanger all devices in the network. Considering the high number of devices connected to network environments, this study has been handled. In this study, it is aimed to train the traffic obtained by using different classification algorithms by creating the network traffic that may occur in the systems in a possible cyber attack. Different cyber attacks were carried out in the created test environment, the characteristics of the network packets obtained during the cyber attacks were extracted and training operations were carried out with commonly used classification algorithms. As a result of the processes, the classification algorithm that provides the best success rate among the classification algorithms used was determined.