Malware detection using XGBoost and Random Forest.

e of the ways to ensure security is to detect malware in computer systems by malware detection methods. Since this entails a lot of financial, time and human costs, the present research intends to rely on extracting useful information from raw data without the need to perform sampling and classification based on these features, costs reduce the listed. In this regard, for each malware sample, a set of content-based features has been calculated using advanced mechanisms. Also, powerful statistical features are considered as a complement to content-based features. Therefore, according to the research findings on the Microsoft malware database called BIG 2015, a cost-effective and fully automated classifier has been presented. In the proposed method using XGB algorithm and Random Forest, the accuracy of the classifier is 99.81 and the predictor error is set to 0.00470. The findings of this study show that the achievement of this research is to determine the superiority of operator replication features, segment ID replication, images extracted from malware over other features. As a result, by using this research in IDS, IPS and native antivirus systems, it is possible to increase the accuracy of malware detection and also reduce malware detection errors and computer crimes.