Security and Communication Networks

In today’s integrated economy, no country’s economic development can be outside the international economic environment. After continuous and rapid development, China’s economy has surpassed Japan’s overall economic level and has become the second largest economic power in the world. But with the advent of the new normal economy in China, the economy has entered a stage of medium- and high-speed development, and business management and cost management of enterprises are also facing the transformation of strategies and systems. Every enterprise and company is trying its best to improve its own cost management system, business model, and cost management strategy. The development of science and technology and the advent of the era of information technology have gradually brought China’s economic development into a green development track, which has put forward greater research topics for enterprise cost management. Therefore, the method for controlling economic management cost in enterprises based on the Coase theorem was proposed.

Cell-free massive multi-input multi-output (MIMO) systems exhibit many characteristics that differ from those of traditional centralized massive MIMO systems, and there are still research gaps in the modeling of cell-free massive MIMO channels. In this paper, a geometry-based stochastic model (GBSM) that combines the double-ring model and hemisphere model to comprehensively consider the distribution of scatterers in the environment was proposed for cell-free massive MIMO channels. Combined with the line-of-sight (LoS) path, single scattering path, and double scattering path components, the channel matrix between the access points (APs) and the user was derived. The proposed model fully considers geometric parameters such as the arrival/departure direction, elevation angle, time delay, and distance, which can accurately characterize the channel. Then, we proved that the traditional channel model, standard block-fading model, and spatial basis expansion model (SBEM) adopted in a cell-free massive MIMO system could not describe the nonstationarity in the space, time, and frequency domains, whereas the proposed GBSM could. Statistical characteristics of the channel were analyzed, including the space cross-correlation function (CCF), time autocorrelation function (ACF), Doppler power spectral density (PSD), level crossing rate (LCR), and average fade duration (AFD). Then, we investigated the proposed model by simulating the space CCF, time ACF, Doppler PSD, LCR, and AFD under the conditions of two different scatterer densities. Through simulations and analyses, some new features of cell-free massive MIMO channels were identified, providing a theoretical basis for in-depth research on cell-free massive MIMO systems. Finally, the measurement-based scenario and the WINNER II channel model are compared to demonstrate that the GBSM is more practical to characterize real cell-free massive MIMO channels.

The Industrial Internet of Things (IIoT) improves productivity and intelligent manufacturing process through revolutionary technology. Due to the complexity of the manufacturing process, cross-domain access is inevitable. Recently, Meng et al. proposed a secure and efficient blockchain-assisted entity authentication mechanism BASA for IIoT cross-domain. In the BASA scheme, the authors utilized identity-based signature (IBS) to realize mutual authentication and the Ephemeral Elliptic Curve Diffie-Hellman (ECDHE) exchange mechanism to negotiate the session key. Due to the inherent key escrow problem of identity-based cryptography (IBC), the key generation center (KGC) can obtain the session key negotiated between two entities distributed in different domains. When KGC is threatened, the security of the session key is worrying. Considering this security concern, based on the BASA scheme, in this article, we first show a secure and efficient certificateless public-key signature (CL-PKS) scheme with anonymity. Then, combined with the ECDHE key exchange mechanism, we give an efficient cross-domain authentication and key agreement scheme CL-BASA with the aid of consortium blockchain. After that, we make security verification by the formal analysis tool, Tamarin, which shows that our CL-BASA is secure. The evaluation demonstrates that our CL-BASA may have a slight disadvantage in storage overhead, but it has obvious advantages than competitor schemes in terms of communication overhead and computational overhead.

The clouds and snow in optical remote sensing images always interfere with the interpretation of remote sensing images, which even makes an entire image unavailable. In general, the proportion of cloud/snow cover in remote sensing images needs to be clarified to improve the utilization of remote sensing images. The metadata of remote sensing image products contains prior knowledge of spatiotemporal information, such as imaging time, latitude and longitude, and altitude. This paper proposes a remote sensing image cloud/snow detection method that fuses spatial and temporal information. The proposed method can combine spatiotemporal information for feature extraction and stitching, thus improving the accuracy of remote sensing image cloud/snow detection. In this study, the proposed method is trained and tested with a large-scale cloud/snow image dataset. The experimental results show that both the temporal or spatial information alone and the fused temporal and spatial information can improve the cloud/snow detection accuracy in remote sensing images. The easy-to-obtain imaging time information can also significantly improve the detection accuracy for cloud/snow. The proposed method can be used to improve the cloud/snow detection effect of any remote sensing image product containing prior knowledge of spatiotemporal information and has a good application prospect.

Nowadays, the industrial Internet of Things (IIoT) is playing a promising role in the optimization of industrial systems. IIoT devices generate a great amount of data that could be used for different applications. Due to the untrusted nature of machine-to-machine (M2M) communication channels, data authenticity and integrity are an important issue that must be addressed. Especially, it is challenging to deal with the privacy disclosure that the heterogeneity of IIoT brings about. In this paper, we propose a privacy-preserving scheme for authenticity in heterogeneous IIoT systems. Our authentication schemes support many kinds of IIoT devices with multicryptographic configurations such as RSA-based, DL-based, ECC-based, and lattice-based cryptosystems. We provide the formalized proof of the unforgeability and privacy of the proposed schemes in the random oracle model. The experimental simulation demonstrates that the proposed schemes are feasible in the heterogeneous IIoT environment.

With the progress of face manipulation techniques, synthesized faces are spreading on the Internet, which raises concerns about potential threats. To prevent these techniques’ abuse, various detection algorithms have been proposed. In this paper, we consider the image’s frequency information, then propose an adaptive filtering algorithm named spatial and adaptive filtering (SAF) Network. SAF is a dual-stream network that considers spatial and frequency domains. In the frequency domain, wavelet transform is used to divide the image into different frequency bands, then an adaptive filter is introduced, which aims to capture more decisive information by giving different weights to different frequencies. To fuse spatial and frequency features, spatial pyramid pooling fusion (SPPF) is proposed, which solves the mismatch of feature maps, and considers the relationship between different patches by attention mechanism. Experiment results show that the performance of SAF is better than the comparison algorithm.