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Authors: Bonan Cuan 1 ; Aliénor Damien 2 ; Claire Delaplace 3 and Mathieu Valois 4

Affiliations: 1 INSA Lyon, CNRS, LIRIS, Lyon and France ; 2 Thales Group, Toulouse, France, CNRS, LAAS, Toulouse and France ; 3 Univ Rennes 1, CNRS, IRISA, 35000 Rennes, France, Univ. Lille, CRIStAL, 59655 Villeneuve d’Ascq and France ; 4 Normandie Univ., UNICAEN, ENSICAEN, CNRS, GREYC, 14000 Caen and France

ISBN: 978-989-758-319-3

Keyword(s): Malicious PDF Detection, SVM, Evasion Attacks, Gradient-Descent, Feature Selections, Adversarial Learning.

Related Ontology Subjects/Areas/Topics: Information and Systems Security ; Intrusion Detection & Prevention

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.

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Paper citation in several formats:
Cuan, B.; Damien, A.; Delaplace, C. and Valois, M. (2018). Malware Detection in PDF Files using Machine Learning.In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2 SECRYPT: SECRYPT, ISBN 978-989-758-319-3, pages 412-419. DOI: 10.5220/0006884705780585

@conference{secrypt18,
author={Bonan Cuan. and Aliénor Damien. and Claire Delaplace. and Mathieu Valois.},
title={Malware Detection in PDF Files using Machine Learning},
booktitle={Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2 SECRYPT: SECRYPT,},
year={2018},
pages={412-419},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006884705780585},
isbn={978-989-758-319-3},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2 SECRYPT: SECRYPT,
TI - Malware Detection in PDF Files using Machine Learning
SN - 978-989-758-319-3
AU - Cuan, B.
AU - Damien, A.
AU - Delaplace, C.
AU - Valois, M.
PY - 2018
SP - 412
EP - 419
DO - 10.5220/0006884705780585

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