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The ability to efficiently detect the software protections used is at a primeto facilitate the selection and application of adequate deob-fuscationtechniques. We present a novel approach that combines semantic reasoningtechniques with ensemble learning classification for the purpose of providing astatic detection framework for obfuscation transformations. By contrast toexisting work, we provide a methodology that can detect multiple layers ofobfuscation, without depending on knowledge of the underlying functionality ofthe training-set used. We also extend our work to detect constructions ofobfuscation transformations, thus providing a fine-grained methodology. To thatend, we provide several studies for the best practices of the use of machinelearning techniques for a scalable and efficient model. According to ourexperimental results and evaluations on obfuscators such as Tigress and OLLVM,our models have up to 91% accuracy on state-of-the-art obfuscationtransformations. Our overall accuracies for their constructions are up to 100%.

Fine-Grained Static Detection of Obfuscation Transforms Using Ensemble-Learning and Semantic Reasoning