# SACT: A New Model of Covert Communication Based on SDN
## 2 Related Work
[23] proposed the theoretical basis of hiding tracking information into time information, which had become the research focus of network watermarking technology. [PERSON] et al. [24] proposed a watermarking technology based on slot centroid flow. The watermark information was embedded by using the slot centroid of the data stream. This method used fixed values to modify the delay sequence, which was not strong in concealment and had weak resistance to time disruption. Therefore, it cannot handle time analysis attack, multi stream attack, and mean square autocorrelation attack. [PERSON] et al. [25] introduced some different schemes of watermarking in anonymity systems and discussed some attacks against it, putting forward some ideas for improving watermarking. [PERSON] et al. [26] proposed a secure multiple watermarking technique based on discrete wavelet transforms (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD). This method resisted different signal processing attacks and performed well in robustness, imperceptibility, capacity, and security. In order to improve the security of the watermark, [PERSON] et al. [27] applied the encryption algorithm based on chaos to the watermark image. The algorithm had exceptional robustness and enough security to deal with all kinds of attacks. In order to further improve the robustness of watermarking scheme, [PERSON] et al. [28] proposed a removal attack of convolutional neural network (CNN). Based on 2D frictional fryer transformation technique (FRFT), [PERSON] et al. [29] proposed a new watermarking technology, which can survive various image processing attacks and protect the embedded digital content. The system introduced an attack model designed by two common image processing attacks to evaluate the strength of the proposed watermarking scheme. The technology of increase and decrease with pseudo random of packet interval delay [30] had a good anti detection ability, but it performed poorly in the network environment with poor stability, resulting in poor accuracy. The sliding window algorithm was applied to the watermarking technology, which improved the resistance and concealment of the carrier data stream to network jitter [31]. However, accuracy and fault tolerance still need to improve. [PERSON] et al. [32] analyzed the positive and negative impacts of SDN on network security, described the availability access control and application-oriented security status of SDN, and emphasized the opportunities and challenges brought by SDN to network security. [PERSON] et al. [33] put forward two new attacks in SDN and expounded the countermeasures against system vulnerabilities of SDN. The optimization of watermarking algorithm is of great significance to the development of anonymous communication. However, these methods only improve the security of the watermark but ignore the performance limitation caused by the difficulty of deployment. In this paper, aiming at the deployment of traditional anonymous tracking model, the watermarking technology is integrated into SDN to improve the concealment of data transmission and solve the deployment problem of anonymous tracing. Our contribution differs significantly from the above work:  
* The convolutional code is applied to watermark tracking in our system, which can effectively reduce error code and improve robustness. In addition, the encoded watermark is compressed and converted to improve channel utilization. It not only improves the security of watermark but also ensures efficiency. Most of the other schemes only focus on the security of watermark but ignore the efficiency of watermark transmission channel. * The combination of watermark and SDN effectively solves the deployment of watermark tracking scheme, reduces the cost of deployment. * Our system dynamically transforms the coding mapping scheme, effectively prevents the interception and analysis of the coding scheme. It is of great significance to solve the problems of attack source tracking, network monitoring, and forensics.