Network covert channels in the Internet of Things (IoT) can conceal device communication behaviors, thereby protecting user privacy and ensuring secure transmission of sensitive data. However, existing active covert channels in IoT are constrained by the low computational power and limited storage of IoT terminal devices, making them unsuitable for complex steganographic algorithms. Moreover, the active covert channel is more vulnerable to targeted detection and blocking by traffic analysis. To address these challenges, this study proposes a passive network storage covert channel (PNSCC) that leverages intermediate IoT nodes. We also present solutions for synchronization and reversible data hiding within the PNSCC. A covert channel kernel module was designed and deployed on smart routers running the OpenWrt system, followed by experimental testing. The results indicate that the PNSCC achieves a relatively high capacity, with a covert data transmission rate of approximately 88.97 bps in real-world network environments. Additionally, it has minimal impact on actual network performance and exhibits resilience against traffic analysis.

By using traffic analysis attacks, attackers track users’ network traffic and analyze user behaviors, such as visited websites and online activities. Users’ personal privacy is at risk. Traffic obfuscation is a new way to protect privacy. Traffic obfuscation is commonly used in censory-circumvention systems to help Internet users bypass online censorship, but recent work has used it to disguise user traffic to evade adversary attacks. However, the current traffic obfuscation methods and rules are relatively simple, can not dynamically adapt to the network environment, and lack of uniform traffic feature similarity evaluation index. In this paper, we propose a generative adversarial network(GAN) model based on temporal convolutional network(TCN). TCN-GAN learns traffic features to achieve the purpose of disguising specific traffic as target traffic. At the same time, we provide the Measurement of Indiscernibility(MoI) for evaluating the difference between generated traffic features and real traffic features. Compared with the existing work, our experimental results show that in machine learning-based traffic analysis attacks, TCN-GAN has better performance on sample quality and mimicry effect, and MoI can be used as an effective index to evaluate the model generation effect.