Employing Linear Algebraic Concepts And Machine Learning Techniques To Dwindle Threats And Malware Functions

Abstract

This research introduces innovative machine learning methodologies aimed at enhancing classification accuracy in complex, nonlinear separable datasets. The research first identifies the effectiveness of nonlinear mapping functions denoted as and#934; in transforming nonlinear separable data into new feature spaces, thereby facilitating clear linear decision boundaries and significantly improving Support Vector Machines (SVM) performance. newlineA key contribution is the integration of Principal Component Analysis (PCA) with SVM to classify normal and anomalous logins within an intrusion detection system. In addition, the research explores an adaptive K-Nearest Neighbors (K-NN) classification approach, where neighborhood sizes and distance metrics are dynamically adjusted, resulting in superior classification accuracy compared to traditional fixed approaches. A comparative analysis of parameter variations, including the parameter in nonlinear mapping functions, provides further insights into the robustness and flexibility of the proposed methods. The research introduces the nonlinear mapping function designed to transform clustered nonlinear data into a linearly separable form, successfully enhancing classification accuracy. Overall, this work advances newline