Integration of Machine Learning Techniques for Intelligent Network Attack Detection

This study compares the effectiveness of logistic regression and linear discriminant analysis classifiers to assess the application of machine learning techniques for identifying network assaults. Through rigorous testing, it was discovered that logistic regression performed better at correctly identifying network threats than linear discriminant analysis. Higher accuracy, precision, and recall were shown by logistic regression, demonstrating its superior performance and flexibility in managing various assault kinds of network. However, Linear Discriminant Analysis displayed significantly worse performance metrics, highlighting its shortcomings in correctly categorizing network attacks. These results demonstrate the superior performance of logistic regression as a classifier for network attack detection, offering useful information for the creation of powerful machine learning-based security systems. However, Linear Discriminant Analysis displayed significantly worse performance metrics, highlighting its shortcomings in correctly identifying network attacks. Even while it showed some capacity, its performance was less accurate and dependable. In this study overall best Performer algorithms are Logistic Regression Classifiers and Less Perform Linear Discriminant Analysis.