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Abstract:We present how we used machine learning techniques to detect malicious behaviours in PDF files. At this aim, we first set up a SVM (Support Machine Vector) classifier that was able to detect 99.7% of malware. However, this classifier was easy to lure with malicious PDF files, which we forged to make them look like clean ones. For instance, we implemented a gradient-descent attack to evade this SVM. This attack was almost 100% successful. Next, we provided counter-measures to this attack: a more elaborated features selection and the use of a threshold allowed us to stop up to 99.99% of this attack. Finally, using adversarial learning techniques, we were able to prevent gradient-descent attacks by iteratively feeding the SVM with malicious forged PDF files. We found that after 3 iterations, every gradient-descent forged PDF file were detected, completely preventing the attack.