Machine Learning Assisted Wiretapping
It is well known, that wiretap codes can be used to protect against a potential eavesdropper in a communication scenario. Asymptotically, they can achieve both vanishing decoding error probability at the legitimate receiver and vanishing leaked information to an eavesdropper. However, under finite blocklength, this does not hold and there is a tradeoff between different code parameters. In this work, it is shown, how machine learning algorithms can be utilized by an eavesdropper to decode finite-blocklength polar wiretap codes successfully. Neural networks and support vector machines are implemented as channel decoders and compared to a reference polar decoder. Simulation results show that the support vector machine decoders can outperform the reference decoder with respect to the bit error ratio.
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