The dataset was used as a support to attack obfuscations. It is the largest obfuscated dataset, with realistic and various binaries and functions. It is made to support other experiments on obfusca...

Protecting sensitive program content is a critical concern in various situations, ranging from legitimate use cases to unethical contexts. Obfuscation is one of the most used techniques to ensure such a protection. Consequently, attackers must first detect and characterize obfuscation before launching any attack against it. This paper investigates the problem of function-level obfuscation detection using graph-based approaches, comparing algorithms, from classical baselines to advanced techniques like Graph Neural Networks (GNN), on different feature choices. We consider various obfuscation types and obfuscators, resulting in two complex datasets. Our findings demonstrate that GNNs need meaningful features that capture aspects of function semantics to outperform baselines. Our approach shows satisfactory results, especially in a challenging 11-class classification task and in two practical binary analysis examples. It highlights how much obfuscation and optimization are intertwined in binary code and that a better comprehension of these two principles are fundamental in order to obtain better detection results.

Laurent Willemez, chercheur en sociologie du droit et de la justice et coauteur de « Sociologie de la magistrature » analyse pour Politis le mythe du "juge rouge" et revient sur la distance entre les ...