TY - JOUR AU - Rkhouya, Safa AU - Chougdali, Khalid PY - 2021/10/25 Y2 - 2024/03/28 TI - Malware Detection Using a Machine-Learning Based Approach JF - International Journal of Information Technology and Applied Sciences (IJITAS) JA - IJITAS VL - 3 IS - 4 SE - Articles DO - 10.52502/ijitas.v3i4.172 UR - https://woasjournals.com/index.php/ijitas/article/view/172 SP - 167-171 AB - <p>The purpose of this research work is to study the usage of machine learning in detecting malware. This paper presents a versatile framework, in which a dataset of more than 130000 files has been analyzed, to train and test four machine learning algorithms: Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting; The performance of each algorithm in malware classification, has been studied based on the: Accuracy, execution time, rate of false positives and false negatives, and area under the Receiver Operating Characteristic curve.</p> ER -