PDF Malware Detection Using Machine Learning

Abstract: With the increasing reliance on digital documents, Portable Document Format (PDF) files have become a common vector for cyberattacks. Attackers exploit the flexibility and rich feature set of PDFs to embed malicious content such as JavaScript, executables, or hidden links, which can compromise user systems upon opening. Traditional signature-based malware detection methods often fail to identify novel or obfuscated threats, highlighting the urgent need for more adaptive and intelligent solutions. This report presents a machine learning-based approach to detect malicious PDF files effectively. We begin by analyzing the structural characteristics of both benign and malicious PDFs, extracting meaningful features such as the presence of embedded JavaScript, object counts, entropy values, and metadata anomalies. These features are then used to train various supervised learning models, including Decision Trees, Random Forests, Support Vector Machines (SVM), and Gradient Boosting algorithms. Emphasis is placed on achieving high detection accuracy while maintaining low false positive rates.