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.

Have you ever wondered if it’s possible to replicate the stealthy behavior of a modern cyber‑attack using artificial intelligence? As part of my research, I focused on the techniques used by a Linux-b...

Disabling password authentication of your OBS WebSocket server can have devastating consequences. We'll attack from the browser to construct an RCE payload on Windows formed from the pixels of an imag...

Key findings  Proofpoint researchers identified a highly targeted email-based campaign targeting fewer than five Proofpoint customers in the United Arab Emirates with a distinct