Fine-Grained Static Detection of Obfuscation Transforms Using Ensemble-Learning and Semantic Reasoning - Grenoble Alpes Cybersecurity Institute Access content directly
Conference Papers Year : 2019

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

Ramtine Tofighi-Shirazi
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Irina Mariuca Asavoae
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Philippe Elbaz-Vincent

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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Dates and versions

hal-02355528 , version 1 (12-11-2019)

Identifiers

  • HAL Id : hal-02355528 , version 1

Cite

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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