Safety assessment model for DoS attacks detection in wireless communication and network OS environments

Wireless networks and communications have witnessed tremendous development and growth in recent periods and up until now, as there is a group of diverse networks such as the well-known wireless communication networks and others that are not linked to an infrastructure such as telephone networks, sensors and wireless networks, especially in important applications that work to send and receive important data and information in relatively unsafe environments, cybersecurity technologies pose an important challenge in protecting unsafe networks in terms of their impact on reducing crime. Detecting hacking in electronic networks and penetration testing. Therefore, these environments must be monitored and protected from hacking and malicious attacks. In this manuscript, a correspondence model is designed to discuss the algorithm in the environment of wireless communication systems and distributed computing by adopting the protocol data enhancement to the network using structured construction and diversity algorithm to improve the effectiveness of the intrusion detection system. Next, a multi-label convolutional neural network model is used to detect business transactions. An CNN was trained on the WSN-DS dataset using 5-Fold in CV technique with three hidden layers. The highest Precision values 0.951 of Grayhole attack for multi-classification.