<![CDATA[The rapid evolution of information technology has created vast opportunities in multiple domains, yet it also brings critical challenges in the realm of cybersecurity, particularly with the growing frequency of malware attacks. Modern malware utilizes advanced evasion and spreading techniques, such as polymorphic and metamorphic transformations, which undermine the performance of conventional detection systems. This research aims to evaluate and compare the effectiveness of several machine learning algorithms optimized through hyperparameter tuning to determine the most accurate and reliable model for malware detection. The study applies a supervised learning approach using labeled data and examines five algorithms: Multilayer Perceptron, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Hist Gradient Boosting. Each model was fine-tuned to identify its optimal configuration, and performance was measured using accuracy, precision, recall, and F1-score. The experiments were conducted on a dataset comprising 58,596 records that had been thoroughly cleaned and preprocessed. The findings indicate that the Multilayer Perceptron achieved superior results, obtaining 99.97% across all evaluation metrics. These outcomes demonstrate the model’s strong potential for reliable malware detection and its suitability for integration into cybersecurity frameworks that demand fast response, high precision, and adaptability to evolving attack patterns.]]>
Copyrights © 2026
