Most machine learning algorithms only have a good recognition rate on balanced datasets. However, in the field of malicious traffic identification, benign traffic on the network is far greater than malicious traffic, and the network traffic dataset is imbalanced, which makes the algorithm have a low identification rate for small categories of malicious traffic samples. This paper presents a traffic sample synthesizing model named Conditional Tabular Traffic Generative Adversarial Network (CTTGAN), which uses a Conditional Tabular Generative Adversarial Network (CTGAN) algorithm to expand the small category traffic samples and balance the dataset in order to improve the malicious traffic identification rate. The CTTGAN model expands and recognizes feature data, which meets the requirements of a machine learning algorithm for training and prediction data. The contributions of this paper are as follows: first, the small category samples are expanded and the traffic dataset is balanced; second, the storage cost and computational complexity are reduced compared to models using image data; third, discrete variables and continuous variables in traffic feature data are processed at the same time, and the data distribution is described well. The experimental results show that the recognition rate of the expanded samples is more than 0.99 in MLP, KNN and SVM algorithms. In addition, the recognition rate of the proposed CTTGAN model is better than the oversampling and undersampling schemes.

Compared with Shamir’s original secret image sharing (SIS), the Chinese-remainder-theorem-based SIS (CRTSIS) generally has the advantages of a lower computation complexity, lossless recovery and no auxiliary encryption. However, general CRTSIS is neither perfect nor ideal, resulting in a narrower range of share pixels than that of secret pixels. In this paper, we propose a practical and lossless CRTSIS based on Asmuth and Bloom’s threshold algorithm. To adapt the original scheme for grayscale images, our scheme shares the high seven bits of each pixel and utilizes the least significant bit (LSB) matching technique to embed the LSBs into the random integer that is generated in the sharing phase. The chosen moduli are all greater than 255 and the share pixels are in the range of [0, 255] by a screening operation. The generated share pixel values are evenly distributed in the range of [0, 255] and the selection of $(k,n)$ threshold is much more flexible, which significantly improves the practicality of CRTSIS. Since color images in RGB mode are made up of three channels, it is easy to extend the scheme to color images. Theoretical analysis and experiments are given to validate the effectiveness of the proposed scheme.

In some particular situations, participants need to recover different secrets both within a group (i.e., intragroup) and between two groups (i.e., intergroup). However, most of the existing multilevel secret sharing (MLSS) and multigroup secret sharing (MGSS) schemes mainly focus on how to protect a secret between one or more groups. In this paper, we propose a polynomial-based scheme to share multiple secret images both within a group and between groups. The random elements’ utilization model of integer linear programming is used to find polynomial coefficients that meet certain conditions so that each participant holds only one shadow image and some of them can recover secrets of both intergroup and intragroup. In addition, our scheme based on polynomials has the advantage of low computational complexity. Theoretical analysis and experiments show that the proposed scheme is feasible and effective.

Secret image sharing has been extensively and thoroughly researched. However, in the social network environment, shadow images are subject to compression or noise pollution during uploading and transmitting, which makes it challenging to recover secrets losslessly. Texts are more suited for transmission in social networks as shadows because of the broad variety of application scenarios and inherent robustness. Through a secret sharing technique of threshold, a secret is encrypted as shadows, where any or more shadows can recover the secret, while less than cannot obtain any information on the secret. In this article, we propose a generative text secret sharing scheme with topic-controlled shadows, which encrypts a secret message as a number of semantically natural shadow texts and controls the topics of shadow texts using bag-of-words models during text generation by the language model. This study also proposes two goal programming models to improve the shadow texts’ topic relevance and fluency. The shadow texts of the proposed scheme satisfy loss tolerance, semantic comprehensibility, topic controllability, and robustness. An ablation study, comparative test, and anti-detection experiment verify the effectiveness of the proposed scheme.