Detect Malware in Portable Document Format Files (PDF) Using Support Vector Machine and Random Decision Forest

Abstract

Portable Document Format is a very powerful type of file to spread malware because it is needed by many people, this makes PDF malware not to be takenlightly. PDF files that have been embeddedwith malware can be Javascript, URL access, media that has been infectedwith malware, etc. With a variety of preventive measures can help to spread, for example in this study using the classification method between dangerous files or not. Two classification methods that have the highest accuracy value based on previous research are Support Vector Machine and Random Forest. There are 500 datasets consistingof 2 classes, namely malicious and notmaliciousand 21 maliciousPDF features as material for the classification process. Based on the calculation of Confusion Matrix as a comparison of the results of the classification of the two methods, the results show that the Random Forest method has better results than Support Vector Machine even though its value is still not perfect.