In the intelligent era of human-computer symbiosis, the use of machine learning method for covert communication confrontation has become a hot topic of network security. The existing covert communication technology focuses on the statistical abnormality of traffic behavior and does not consider the sensory abnormality of security censors, so it faces the core problem of lack of cognitive ability. In order to further improve the concealment of communication, a game method of “cognitive deception” is proposed, which is aimed at eliminating the anomaly of traffic in both behavioral and cognitive dimensions. Accordingly, a Wasserstein Generative Adversarial Network of Covert Channel (WCCGAN) model is established. The model uses the constraint sampling of cognitive priors to construct the constraint mechanism of “functional equivalence” and “cognitive equivalence” and is trained by a dynamic strategy updating learning algorithm. Among them, the generative module adopts joint expression learning which integrates network protocol knowledge to improve the expressiveness and discriminability of traffic cognitive features. The equivalent module guides the discriminant module to learn the pragmatic relevance features through the activity loss function of traffic and the application loss function of protocol for end-to-end training. The experimental results show that WCCGAN can directly synthesize traffic with comprehensive concealment ability, and its behavior concealment and cognitive deception are as high as 86.2% and 96.7%, respectively. Moreover, the model has good convergence and generalization ability and does not depend on specific assumptions and specific covert algorithms, which realizes a new paradigm of cognitive game in covert communication.

中文翻译：

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基于生成对抗网络的认知隐蔽流量合成方法

在人机共生的智能时代，利用机器学习方法进行隐蔽的通信对抗已经成为网络安全的热门话题。现有的隐蔽通信技术侧重于交通行为的统计异常，没有考虑安检人员的感官异常，面临认知能力不足的核心问题。为了进一步提高通信的隐蔽性，提出了一种“认知欺骗”的博弈方法，旨在消除行为和认知维度上的交通异常。据此，建立了Wasserstein Generative Adversarial Network of Covert Channel（WCCGAN）模型。该模型利用认知先验的约束抽样构建“功能等价”和“认知等价”的约束机制，并通过动态策略更新学习算法进行训练。其中，生成模块采用联合表达学习，结合网络协议知识，提高交通认知特征的表达性和可辨别性。等效模块通过流量的活动损失函数和协议的应用损失函数引导判别模块学习语用相关特征，进行端到端的训练。实验结果表明，WCCGAN可以直接合成具有综合隐蔽能力的流量，其行为隐蔽率和认知欺骗率分别高达86.2%和96.7%。而且，