B. Anckaert, H. Mariusz, R. Jakubowski, and . Venkatesan, Proteus: virtualization for diversified tamper-resistance, Proceedings of the Sixth ACM Workshop on Digital Rights Management, pp.47-58, 2006.

R. Baldoni, E. Coppa, D. Cono-d'elia, C. Demetrescu, and I. Finocchi, A survey of symbolic execution techniques, ACM Comput. Surv, vol.51, issue.3, 2018.

S. Banescu, C. S. Collberg, V. Ganesh, Z. Newsham, and A. Pretschner, Code obfuscation against symbolic execution attacks, Proceedings of the 32nd Annual Conference on Computer Security Applications, ACSAC 2016, pp.189-200, 2016.

S. Banescu, C. S. Collberg, and A. Pretschner, Predicting the resilience of obfuscated code against symbolic execution attacks via machine learning, 26th USENIX Security Symposium, pp.661-678, 2017.

S. Bardin, R. David, and J. Marion, Backward-bounded DSE: targeting infeasibility questions on obfuscated codes, 2017 IEEE Symposium on Security and Privacy, pp.633-651, 2017.
URL : https://hal.archives-ouvertes.fr/cea-01808887

F. Biondi, M. A. Enescu, T. Given-wilson, and A. Legay, Lamine Noureddine, and Vivek Verma. Effective, efficient, and robust packing detection and classification, Computers & Security, vol.85, pp.436-451, 2019.

M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, Learning multi-label scene classification, Pattern Recognition, vol.37, issue.9, pp.1757-1771, 2004.

L. Breiman, Bagging predictors, Machine Learning, vol.24, pp.123-140, 1996.

J. Cappaert and B. Preneel, A general model for hiding control flow, Proceedings of the 10th ACM Workshop on Digital Rights Management, pp.35-42, 2010.

S. Chow, X. Yuan, H. Gu, V. A. Johnson, and . Zakharov, An approach to the obfuscation of control-flow of sequential computer programs, Information Security, 4th International Conference, vol.2200, pp.144-155, 2001.

C. Collberg, S. Martin, J. Myers, B. Zimmerman, P. Krajca et al., The Tigress C Diversifier/Obfuscator

C. Collberg and J. Nagra, Surreptitious Software: Obfuscation, Watermarking, and Tamperproofing for Software Protection, 2009.

C. Collberg, C. Thomborson, and D. Low, A taxonomy of obfuscating transformations, 1997.

C. S. Collberg, C. D. Thomborson, and D. Low, Manufacturing cheap, resilient, and stealthy opaque constructs, POPL '98, Proceedings of the 25th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, pp.184-196, 1998.

B. Conte,

B. Coppens, B. D. Sutter, and J. Maebe, Feedback-driven binary code diversification, TACO, vol.9, issue.4, 2013.

F. Desclaux, Miasm : Framework de reverse engineering, 2012.

T. G. Dietterich, Ensemble methods in machine learning, Proceedings of the First International Workshop on Multiple Classifier Systems, MCS '00, pp.1-15, 2000.

Y. Freund, Boosting a weak learning algorithm by majority, Proceedings of the Third Annual Workshop on Computational Learning Theory, COLT 1990, pp.202-216, 1990.

Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, Machine Learning, Proceedings of the Thirteenth International Conference (ICML '96), pp.148-156, 1996.

N. Friedman, D. Geiger, and M. Goldszmidt, Bayesian network classifiers, Mach. Learn, vol.29, issue.2-3, pp.131-163, 1997.

S. Ghosh, J. Hiser, and J. W. Davidson, A secure and robust approach to software tamper resistance, Information Hiding -12th International Conference, IH 2010, vol.6387, pp.33-47, 2010.

S. Godbole and S. Sarawagi, Discriminative methods for multilabeled classification, Advances in Knowledge Discovery and Data Mining, 8th Pacific-Asia Conference, vol.3056, pp.22-30, 2004.

I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)

. Springer-verlag, , 2006.

T. Hastie, R. Tibshirani, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, 2nd Edition. Springer series in statistics, 2009.

S. Howard,

M. James, Classification Algorithms, 1985.

P. Junod, J. Rinaldini, J. Wehrli, and J. Michielin, Obfuscator-LLVM -software protection for the masses, Proceedings of the IEEE/ACM 1st International Workshop on Software Protection, SPRO'15, pp.3-9, 2015.

Y. Kanzaki, A. Monden, M. Nakamura, and K. Matsumoto, Exploiting self-modification mechanism for program protection, 27th International Computer Software and Applications Conference (COMPSAC 2003): Design and Assessment of Trustworthy Software-Based Systems, p.170, 2003.

T. Samuel, P. M. King, Y. Chen, C. Wang, H. J. Verbowski et al., Subvirt: Implementing malware with virtual machines, 2006 IEEE Symposium on Security and Privacy, pp.314-327, 2006.

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol.95, pp.1137-1145, 1995.

S. B. Kotsiantis, Supervised machine learning: A review of classification techniques, Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, pp.3-24, 2007.

B. Sotiris and . Kotsiantis, Supervised machine learning: A review of classification techniques, Informatica (Slovenia), vol.31, issue.3, pp.249-268, 2007.

A. Lakhotia, D. R. Boccardo, A. Singh, and A. Manacero, Context-sensitive analysis without calling-context. Higher-Order and Symbolic Computation, vol.23, pp.275-313, 2010.

T. Lazlo and A. Kiss, Obfuscating c++ programs via control flow flattening

T. Li and M. Ogihara, Detecting emotion in music, ISMIR 2003, 4th International Conference on Music Information Retrieval, 2003.

C. Linn, K. Saumya, and . Debray, Obfuscation of executable code to improve resistance to static disassembly, Proceedings of the 10th ACM Conference on Computer and Communications Security, pp.290-299, 2003.

R. Maclin and D. W. Opitz, Popular ensemble methods: An empirical study, 2011.

M. Madou, B. Anckaert, P. Moseley, K. Saumya, B. D. Debray et al., Software protection through dynamic code mutation, Information Security Applications, 6th International Workshop, WISA 2005, vol.3786, pp.194-206, 2005.

C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, 2008.

J. Ming, D. Xu, L. Wang, and D. Wu, LOOP: logic-oriented opaque predicate detection in obfuscated binary code, Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp.757-768, 2015.

A. Monden, A. Monsifrot, and C. D. Thomborson, A framework for obfuscated interpretation, ACSW Workshops -the Australasian Information Security Workshop (AISW2004), pp.7-16, 2004.

M. Ollivier, S. Bardin, R. Bonichon, and J. Marion, How to kill symbolic deobfuscation for free; or unleashing the potential of path-oriented protections, Proceedings of the 35th Annual Computer Security Applications Conference, ACSAC 2019, 2019.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

I. V. Popov, K. Saumya, G. R. Debray, and . Andrews, Binary obfuscation using signals, Proceedings of the 16th USENIX Security Symposium, 2007.

M. D. Preda, M. Madou, K. De-bosschere, and R. Giacobazzi, Opaque predicates detection by abstract interpretation, Algebraic Methodology and Software Technology, 11th International Conference, pp.81-95, 2006.

, GNU Project. GNU Core Utilities, 2002.

J. Read, B. Pfahringer, G. Holmes, and E. Frank, Classifier chains for multi-label classification, Machine Learning, vol.85, issue.3, pp.333-359, 2011.

J. Read, B. Pfahringer, G. Holmes, and E. Frank, Classifier chains for multi-label classification, Mach. Learn, vol.85, issue.3, pp.333-359, 2011.

L. Rokach, Ensemble-based classifiers, Artif. Intell. Rev, vol.33, issue.1-2, pp.1-39, 2010.

L. Rokach and O. Maimon, Data Mining With Decision Trees: Theory and Applications, 2014.

A. Salem and S. Banescu, Metadata recovery from obfuscated programs using machine learning, Proceedings of the 6th Workshop on Software Security, Protection, and Reverse Engineering, vol.2016, pp.1-1, 2016.

J. Salwan, S. Bardin, and M. Potet, Symbolic deobfuscation: From virtualized code back to the original, Detection of Intrusions and Malware, and Vulnerability Assessment -15th International Conference, DIMVA 2018, pp.372-392, 2018.

S. Schrittwieser and S. Katzenbeisser, Code obfuscation against static and dynamic reverse engineering, Information Hiding -13th International Conference, vol.6958, pp.270-284, 2011.

S. Schrittwieser, S. Katzenbeisser, J. Kinder, G. Merzdovnik, and E. R. Weippl, Protecting software through obfuscation: Can it keep pace with progress in code analysis?, ACM Comput. Surv, vol.49, issue.1, 2016.

P. Smyth and D. H. Wolpert, Linearly combining density estimators via stacking, Machine Learning, vol.36, pp.59-83, 1999.

X. Su, M. Taghi, R. Khoshgoftaar, and . Greiner, Making an accurate classifier ensemble by voting on classifications from imputed learning sets, vol.IJIDS, pp.301-322, 2009.

L. Sun, S. Versteeg, S. Boztas, and T. Yann, Pattern recognition techniques for the classification of malware packers, Information Security and Privacy -15th Australasian Conference, vol.6168, pp.370-390, 2010.

B. D. Sutter, B. Anckaert, J. Geiregat, D. Chanet, and K. Bosschere, Instruction set limitation in support of software diversity, Information Security and Cryptology -ICISC 2008, 11th International Conference, vol.5461, pp.152-165, 2008.

R. Tofighi-shirazi, I. M?riuca-as?voae, P. Elbaz-vincent, and T. Le, Defeating Opaque Predicates Statically through Machine Learning and Binary Analysis, 3rd International Workshop on Software PROtection, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02269192

R. Tofighi-shirazi, M. Christofi, P. Elbaz-vincent, and T. H. Le, DoSE: Deobfuscation based on Semantic Equivalence, Proceedings of the 8th Software Security, Protection, and Reverse Engineering Workshop, vol.1, pp.1-1, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01964550

G. Tsoumakas and I. Katakis, Multi-label classification: An overview, vol.IJDWM, pp.1-13, 2007.

K. Sharath, . Udupa, K. Saumya, M. Debray, and . Madou, Deobfuscation: Reverse engineering obfuscated code, 12th Working Conference on Reverse Engineering, pp.45-54, 2005.

I. Xabier-ugarte-pedrero, P. G. Santos, M. Bringas, J. Gastesi, and . Esparza, Semi-supervised learning for packed executable detection, 5th International Conference on Network and System Security, pp.342-346, 2011.

Z. Vrba, P. Halvorsen, and C. Griwodz, Program obfuscation by strong cryptography, ARES 2010, Fifth International Conference on Availability, Reliability and Security, pp.242-247, 2010.

C. Wang, J. Hill, J. C. Knight, and J. W. Davidson, Protection of software-based survivability mechanisms, 2001 International Conference on Dependable Systems and Networks (DSN 2001) (formerly: FTCS), pp.193-202, 2001.

H. S. Warren, Hacker's Delight, 2012.

D. Xu, J. Ming, and D. Wu, Generalized dynamic opaque predicates: A new control flow obfuscation method, Information Security -19th International Conference, pp.323-342, 2016.

H. Xu, Y. Zhou, Y. Kang, F. Tu, and M. R. Lyu, Manufacturing resilient bi-opaque predicates against symbolic execution, 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2018, pp.666-677, 2018.

B. Yadegari, B. Johannesmeyer, B. Whitely, and S. Debray, A generic approach to automatic deobfuscation of executable code, 2015 IEEE Symposium on Security and Privacy, pp.674-691, 2015.

M. Zhang, Y. Li, X. Liu, and X. Geng, Binary relevance for multi-label learning: An overview, Front. Comput. Sci, vol.12, issue.2, pp.191-202, 2018.

M. Zhang and Z. Zhou, Multilabel neural networks with applications to functional genomics and text categorization, IEEE Trans. on Knowl. and Data Eng, vol.18, issue.10, pp.1338-1351, 2006.

M. Zhang and Z. Zhou, Ml-knn: A lazy learning approach to multilabel learning, Pattern Recogn, vol.40, issue.7, pp.2038-2048, 2007.