Real Time Eavesdropping Attack Detection by Multivariate Correlation Analysis

Using the multivariate correlation analysis technique, Frothy Disturbance Intrusion Detection Systems (FIDSs) can be crucial in identifying and stopping security threats. Understanding the importance of Frothy Disturbance Intrusion Detection Systems (FIDSs) in protecting various internet-connected domains, we concentrate on modifying traditional intrusion detection techniques for the environment, which has limitations like memory and battery life. In this study, we create a lightweight attack detection technique using the Multivariate Correlation Analysis algorithm and a supervised machine learning-based FIDS. Using a combination of two or three complex characteristics, we illustrate through simulations how effective the suggested Multivariate Correlation Analysis-based FIDS classifier is, exhibiting good performance in terms of detection time and classification accuracy. This strategy has the potential to improve application security by effectively meeting the unique limitations and specifications of intrusion detection in situations with limited resources.