计算机与现代化 ›› 2021, Vol. 0 ›› Issue (07): 120-126.

• 信息安全 • 上一篇    

基于生成式对抗网络的拟态蜜罐特征生成方法

  

  1. (中国石油大学(华东)计算机科学与技术学院,山东青岛266580)
  • 出版日期:2021-08-02 发布日期:2021-08-02
  • 作者简介:刘祎豪(1996—),男,江西赣州人,硕士研究生,研究方向:深度学习,网络安全,E-mail: 984854217@qq.com。
  • 基金资助:
    山东省自然科学基金资助项目(ZR2019MF034); 国家自然科学基金资助项目(61772551)

A Method to Generate Features of Mimicry Honeypot Based on Generative Adversarial Networks

  1. (College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China)
  • Online:2021-08-02 Published:2021-08-02

摘要: 拟态蜜罐借鉴生物拟态博弈思想,是一种综合运用“蜜罐模拟服务特征”的保护色机制和“服务模拟蜜罐特征”的警戒色机制进行诱骗博弈的动态蜜罐技术,其核心策略是特征生成与演化。生成式对抗网络(GAN)则是一种特征生成方法,它通过生成器与判别器之间的对抗博弈,使生成器生成的数据达到“以假乱真”的效果,其对抗博弈的思想与拟态蜜罐思想极为相近。本文提出一种基于生成式对抗网络的拟态蜜罐特征生成方法MMHP-GAN(Mimicry honeypot-GAN),通过对MMHP-GAN的结构及参数优化训练,产生真假难辨的蜜罐或服务新特征。实验表明,通过该方法生成的特征数据进行演化,服务可以有效抵抗攻击,并且通过对比,本文的方案要优于当前已有的特征生成方案。

关键词: 蜜罐, 拟态蜜罐, 生成式对抗网络, 主动网络防御, 对抗博弈, 特征生成

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