Abstract: Mimicry honeypot is a kind of dynamic honeypot technology, which refers to the idea of biological mimicry game, Comprehensively uses the protective color mechanism of "honeypot simulate service features" and the warning color mechanism of "service simulate honeypot features" to carry on the decoy game. Its core strategy is feature generation and evolution. Generative Adversarial Networks(GAN) is a feature generation method, which can make the data generated by generator reach the effect of "mix the spurious with the genuine" through the antagonistic game between generator and discriminator. The idea of antagonistic game is very similar to the idea of mimicry honeypot. In this paper, based on generative adversarial networks, a method for feature generation of mimicry honeypot(MMHP-GAN) is proposed. By optimizing the structure and parameters of MMHP-GAN, new features of honeypot or service can be generated, which are difficult to distinguish between true and false. The experiment shows that through the evolution of feature data generated by this method, the service can effectively resist attacks, and by comparison, the scheme proposed in this paper is better than the existing scheme for feature generation.

Key words: honeypot, mimicry honeypot, generative adversarial networks, active network defense, antagonistic game, feature generation