Andre Ghazali Armi

Visualization has a function so that we can see the malware in grayscale form which consists of data in the form of a collection of hexadecimal numbers which are converted into decimal. Malware classification is a way to identify and classify malware based on their respective groups. Random forest is one of the many classification methods used for this case. Local Binary pattern used for feature extraction process from existing data. The system is trained and tested using 1000 data from 10 different family malware with a comparison of 8: 2 training and test data. In this study, we utilized an approach of converting a malware binary into an image and use Random Forest to classify various malware families. The resulting accuracy of 0.99-0.995 exhibits the effectivess of the method in detecting malware.