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

arXiv: https://arxiv.org/abs/1911.07523 · HTML: https://mlsanity.com/paper/1911.07523/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Identifying Obfuscated Code through Graph-Based Semantic Analysis of Binary Code](https://mlsanity.com/paper/2504.01481/) (2025)

## Abstract (arXiv)

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%.