Investigating the Impact of Training and Testing Ratios on the Performance of an AI-Based Malware Detector using MATLAB Carlo N. Romero (a*), Matt Ervin G. Mital (a) Zagie D. Rostata (a) Mark Angelo M. Martinez (a)
a) College of Engineering, Our Lady of Fatima University - Quezon City Philippines
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.
Keywords: Malware Detector- Artificial Intelligence- MATLAB-Based Systems- AI-Based System
