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

Abstract : The ability to efficiently detect the software protections used is at a prime to facilitate the selection and application of adequate deob-fuscation techniques. We present a novel approach that combines semantic reasoning techniques with ensemble learning classification for the purpose of providing a static detection framework for obfuscation transformations. By contrast to existing work, we provide a methodology that can detect multiple layers of obfuscation, without depending on knowledge of the underlying functionality of the training-set used. We also extend our work to detect constructions of obfuscation transformations, thus providing a fine-grained methodology. To that end, we provide several studies for the best practices of the use of machine learning techniques for a scalable and efficient model. According to our experimental results and evaluations on obfuscators such as Tigress and OLLVM, our models have up to 91% accuracy on state-of-the-art obfuscation transformations. Our overall accuracies for their constructions are up to 100%.
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Contributor : Ramtine Tofighi-Shirazi Connect in order to contact the contributor
Submitted on : Tuesday, November 12, 2019 - 11:26:39 AM
Last modification on : Tuesday, May 11, 2021 - 11:36:06 AM
Long-term archiving on: : Thursday, February 13, 2020 - 12:30:50 PM


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  • HAL Id : hal-02355528, version 1



Ramtine Tofighi-Shirazi, Irina Mariuca Asavoae, Philippe Elbaz-Vincent. Fine-Grained Static Detection of Obfuscation Transforms Using Ensemble-Learning and Semantic Reasoning. Software Security, Protection, and Reverse Engineering Workshop (SSPREW9), Dec 2019, San Juan, United States. ⟨hal-02355528⟩



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