  Due to the popularity of portable document format (PDF) and increasing number
of vulnerabilities in major PDF viewer applications, malware writers continue
to use it to deliver malware via web downloads, email attachments and other
methods in both targeted and non-targeted attacks. The topic on how to
effectively block malicious PDF documents has received huge research interests
in both cyber security industry and academia with no sign of slowing down. In
this paper, we propose a novel approach based on a multilayer perceptron (MLP)
neural network model, termed MLPdf, for the detection of PDF based malware.
More specifically, the MLPdf model uses a backpropagation algorithm with
stochastic gradient decent search for model update. A group of high quality
features are extracted from two real-world datasets which comprise around
105000 benign and malicious PDF documents. Evaluation results indicate that the
proposed MLPdf approach exhibits excellent performance which significantly
outperforms all evaluated eight well known commercial anti-virus scanners with
a much higher true positive rate of 95.12% achieved while maintaining a very
low false positive rate of 0.08%.
