A comprehensive study on high performance malware classifiers based on machine learning algorithms - Saifaldeen H. A., Karam H. Thanoon (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Malware as a malicious software has been developed and became an interest issue that take great attention of the researchers and companies that delt with data security. Therefore, determining the classes of these malwares is very important to detect further newer or modified versions that are continuously developed. Many classifiers had been developed to implement them to build newer detectors to that are able to secure data. This paper, as comprehensive study illustrates the performance of the most common malwares and highest performance classifiers that were recently proposed. The study shows that there are three proposed classifiers with highest accuracy. They are: Random forest, Support Vector Machine and x-gradient boots. The number of features had a great effect in classification process in which the accuracy decreased if the features number increased. Additional techniques may enhance classification accuracy such as New Feature Engineering.