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A Lightweight Modulation Classification Network Resisting White Box Gradient Attacks

Improving the attack resistance of the modulation classification model is an important means to improve the security of the physical layer of the Internet of Things (IoT). In this paper, a binary modulation classification defense network (BMCDN) was proposed, which has the advantages of small model scale and strong immunity to white box gradient attacks. Specifically, an end-to-end modulation signal recognition network that directly recognizes the form of the signal sequence is constructed, and its parameters are quantized to 1 bit to obtain the advantages of low memory usage and fast calculation speed. The gradient of the quantized parameter is directly transferred to the original parameter to realize the gradient concealment and achieve the effect of effectively defending against the white box gradient attack. Experimental results show that BMCDN obtains significant immune performance against white box gradient attacks while achieving a scale reduction of 6 times.