Circumventing the Defense against Modulation Classification Attacks.

Modulation classification (MC) has a wide range of applications in spectrum sharing, management, and enforcement and can also be used by an adversary to launch traffic analysis or selective jamming. While recent modulation obfuscation techniques show promising results in mitigating MC attacks, in this paper we develop a novel convolution neural network (CNN)-based model to attack those defenses and successfully identify the true modulation scheme. Our extensive simulation and over-the-air experiments using show that our classification technique achieves around 85 - 99% accuracy for SNR levels 0 dB and above. Furthermore, our results demonstrate that the proposed model can effectively differentiate between obfuscated and non-obfuscated symbols, even when a transmitter switches between them as a new defense mechanism, achieving an accuracy of 95%.