Improved Intrusion Detection in Wireless Networks using Random Forest and LDA

The Network Intrusion Detection System (NIDS) monitors network traffic for suspicious activity and notifies users when it is detected, much as a system call sleep and waken in system software. High levels of internet security are required for transactions involving large amounts of data. For any individual or company, keeping an eye on the infiltration is a difficult endeavor. The user needs more tools to assist safeguard the network environment as hackers become more common and skilled. A number of methods have recently been developed to identify intrusions in wireless networks. It is difficult to extract incursion behavior features from a high-dimensional dataset using traditional methods. Furthermore, the traditional approach had a higher time complexity and less accuracy. Like human intelligence, machine learning (ML), a relatively recent family of artificial intelligence techniques, looks for the best course of action based on training. This work's thorough assessment of the effectiveness of LDA-based NIDS is one of its main contributions.