Advanced intrusion detection using ml on webapp and SQL traffic: A correlated dataset model

A web application host, a MySQL database server, and a Datiphy appliance node placed between the two are the two sources of traffic that will be collected in this project. Through our examination of these two datasets as well as an additional dataset that is correlated with them, we have proven that the accuracy achieved with the correlated dataset using algorithms like decision trees and rule-based approaches is comparable to that of a neural network algorithm, but with substantially better performance. Newly interwoven technology and diverse systems have emerged as a result of the digital transformation of both general- and special-purpose networks., as our dependence on networked technologies has grown. Cyberattacks have grown in both frequency and sophistication in recent years, in part because of the dynamic given the current state of technology and the exponential growth of connected devices. The capacity to analyses network traffic using Intrusion Detection Systems (IDS) is a crucial component of any networking security toolset. Using ML and DL techniques, researchers have created intrusion detection systems (IDS) to cope with new and zero-day cyberattacks, which are becoming more common and sophisticated. But developing IDS is difficult since there aren't enough big, realistic, and current datasets.