As cross-border access becomes more frequent, traditional perimeter-based network security models can no longer cope with evolving security requirements. Zero trust is a novel paradigm for cybersecurity based on the core concept of “never trust, always verify”. It attempts to protect against security risks related to internal threats by eliminating the demarcations between the internal and external network of traditional network perimeters. Nevertheless, research on the theory and application of zero trust is still in its infancy, and more extensive research is necessary to facilitate a deeper understanding of the paradigm in academia and the industry. In this paper, trust in cybersecurity is discussed, following which the origin, concepts, and principles related to zero trust are elaborated on. The characteristics, strengths, and weaknesses of the existing research are analysed in the context of zero trust achievements and their technical applications in Cloud and IoT environments. Finally, to support the development and application of zero trust in the future, the concept and its current challenges are analysed.

Convolution and self-attention are two powerful techniques for multi-source remote sensing (RS) data fusion that have been widely adopted in Earth observation tasks. However, Convolutional Neural Networks (CNNs) are inadequate for fully mining contextual information and representing the sequence attributes of spectral signatures. Additionally, the specific self-attention mechanism often comes with high computation costs, which hinders its application in the field of RS. To overcome the above limitations, this paper proposes a unified framework called “Mixing Self-Attention and Convolution Network" for comprehensive feature extraction and efficient feature fusion. First, the proposed MACN utilizes two adaptive CNN encoders (ACEs) to extract shallow convolutional features from multi-source RS data. Secondly, taking the complexity and varying scales of RS data into account, the proposed mixing self-attention and convolution transformer (MACT) layer achieves local and global multiscale perception through an elegant integration of self-attention and convolution. MACT can extract abundant spatial and high-dimensional information (e.g., spectral and elevation information) while maintaining minimal computational overhead compared to pure convolution or self-attention counterparts. Finally, a multi-source cross-guided fusion (MCGF) module is designed to achieve deep fusion of multi-source RS data features. MCGF utilizes a carefully designed cross-modal attention mechanism to capture the interaction between multi-source data and aggregate contextual information. Extensive tests on six public RS datasets have shown that our method outperforms other multi-source fusion models, delivering state-of-the-art results on multiple RS data fusion tasks without specific tuning. The source code of the proposed method will be available publicly at https://github.com/like413/MACN.

A multiscale and multidirectional network named the Contourlet convolutional neural network (CCNN) is proposed for synthetic aperture radar (SAR) image despeckling. SAR image resolution is not higher than that of optical images. If the network depth is increased blindly, the SAR image detail information flow will become quite weak, resulting in severe vanishing/exploding gradients. In this paper, a multiscale and multidirectional convolutional neural network is constructed, in which a single-stream structure of convolutional layers is replaced with a multiple-stream structure to extract image features with multidirectional and multiscale properties, thus significantly improving the despeckling performance. With the help of the Contourlet, the CCNN is designed with multiple independent subnetworks to respectively capture abstract features of an image in a certain frequency and direction band. The CCNN can increase the number of convolutional layers by increasing the number of subnetworks, which makes the CCNN not only have enough convolutional layers to capture the SAR image features, but also overcome the problem of vanishing/exploding gradients caused by deepening the networks. Extensive quantitative and qualitative evaluations of synthetic and real SAR images show the superiority of our proposed method over the state-of-the-art speckle reduction method.