Investigating the Impact of Training and Testing Ratios on the Performance of an AI-Based Malware Detector using MATLAB

Abstract

This research investigates the impact of the training and testing ratios on the performance of an AI-Based Malware Detector using MATLAB. The experiments through MATLAB have shown that higher training percentage means that a larger portion of dataset for training the model have been used while a lower training percentage shows that a large portion of the dataset reserved for testing the model’s performance. The exploration of the influence of training and testing ratios also have been able to determine the performance of an AI-Based Malware Detector. The results give to determining the relationship between training and testing ratios and the effectiveness of the malware detection system.