  Machine learning (ML)-based malware detection systems are becoming
increasingly important as malware threats increase and get more sophisticated.
PDF files are often used as vectors for phishing attacks because they are
widely regarded as trustworthy data resources, and are accessible across
different platforms. Therefore, researchers have developed many different PDF
malware detection methods. Performance in detecting PDF malware is greatly
influenced by feature selection. In this research, we propose a small features
set that don't require too much domain knowledge of the PDF file. We evaluate
proposed features with six different machine learning models. We report the
best accuracy of 99.75% when using Random Forest model. Our proposed feature
set, which consists of just 12 features, is one of the most conciseness in the
field of PDF malware detection. Despite its modest size, we obtain comparable
results to state-of-the-art that employ a much larger set of features.
