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Conference Papers Year : 2018

Malware Detection in PDF Files Using Machine Learning


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.
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Dates and versions

hal-01704766 , version 1 (08-02-2018)
hal-01704766 , version 2 (20-08-2018)


  • HAL Id : hal-01704766 , version 2


Bonan Cuan, Aliénor Damien, Claire Delaplace, Mathieu Valois. Malware Detection in PDF Files Using Machine Learning. SECRYPT 2018 - 15th International Conference on Security and Cryptography, Jul 2018, Porto, Portugal. 8p. ⟨hal-01704766v2⟩
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