Statistical Evaluation of Network Packets in an Intrusion Detection Mechanism Using ML and DL Techniques

Abstract

Network packet diagnoses are analyzed to predict the overall network diagnosis of the packet. The network port handles the multiple transmissions that request the use of the single physical network infrastructure. This process follows up on the send and receive data transmissions, where the network port follows the transmission requests. Tx-Rx packets are predicted using the IP address’s packet state. To diagnose the network condition from source to destination, the network port packet zone has to be analyzed. Some malicious network traffic transmitted across the network damages and harms the server. Tx-Rx packets generally occur when the client starts the connection to the server. The client and server exchange the message connection, in which the client requests a connection for sending synchronizing messages to the server side. An IDS analyses network traffic conditions, which are determined by ML and DL techniques. The main focus of this proposed paper is on determining the network-congested traffic attack and normal state by analyzing network port packets, which determine the IDS in those packets using the UNR-IDD dataset respectively. From this approach, the best prediction technique is considered to determine the network packet condition for IDS.