POSTER: A Tough Nut to Crack: Attempting to Break Modulation Obfuscation

Abstract

Despite being primarily developed for spectrum management, sharing, and enforcement in civilian and military applications, modulation classification can be exploited by an adversary to threaten user privacy (e.g., via traffic analysis), or launch jamming and spoofing attacks. Several existing works study how an adversary can still classify the user traffic despite obfuscation techniques at upper layers, but little work has been done on how an adversary can classify the "modulation scheme'' when it is obfuscated at the physical layer. In this respect, we aim to study how to break the state-of-the-art modulation obfuscation schemes by applying various machine learning (ML) methods. Our preliminary results show that common ML techniques perform poorly in correctly classifying an obfuscated modulation scheme except for the random forest method (with a score as much as twice the other techniques we consider), providing insights on why other techniques, e.g., deep learning, might be more promising for finding underlying correlations.