This PDF file contains the front matter associated with SPIE Proceedings Volume 12175, including the Title Page, Copyright Information, and Table of Contents.

Modulation recognition is a key technology in the field of communication, especially in the field of non cooperative communication. It can realize the automatic recognition and classification of unknown signal modulation without prior knowledge. With the rapid development of modern communication technology, modulation methods become complex and diverse, and modulation recognition technology is also facing more challenges. Although there are relevant studies, there are still many problems and research work to be solved and improved. Based on this, a modulation recognition method of wireless communication digital signal considering multi-dimensional characteristics is proposed. By extracting multidimensional characteristic parameters, constructing multi-dimensional characteristic signal modulation decision tree model and wireless communication digital signal modulation recognition algorithm based on multi-dimensional characteristic model, the modulation mode recognition of digital signal is realized. The experiment shows that compared with the traditional methods, this recognition method has more advantages in the overall modulation recognition accuracy when the signal-to-noise ratio is rising.

With the development of the network, people pay more and more attention to network security. Network attack assessment is a very important item in network security assessment. Starting from three aspects: network attack evaluation attribute index selection, index weight determination, and network attack evaluation model establishment, based on the shortcomings of existing evaluation methods, this paper proposes a variable weight evaluation algorithm and a network attack evaluation model based on TOPSIS. Spatio-temporal correlation attributes are introduced into the attack effect evaluation index system, and a method of fusion of constant weight and variable weight is proposed to determine the weight of the index system. While ensuring the rationality of the indicator system, the compensation mechanism between indicators is overcome. Finally, the algorithm was verified through simulation experiments, and the experimental results show that the algorithm improves the accuracy and effectiveness of the evaluation.

As the existing literatures show, deep learning based steganalysis has achieved much better detection performance compared to traditional hand-crafted feature based methods. However, most methods based on deep learning are quite vulnerable to keep great performance under the image-source mismatch scenario, which is the inconsistency of the source of the training set and testing set. To improve this drawback, we specially design the architecture of the proposed network. Moreover, we first introduce the bilinear pooling into steganalysis to reduce the data source dependence of the model for further improving the performance. Experimental results show that the proposed work can outperform other networks in the image-source mismatch scenario.

It is difficult to improve underwater acoustic communication due to the long delay extension of underwater acoustic channel and serious selective fading in frequency domain. At the same time, there is residual redundancy in the actual source of underwater acoustic communication, which is difficult to be utilized by traditional methods, resulting in a certain broadband waste. In order to improve the reliability of underwater acoustic communication, a joint coding method based on Polar code is proposed in this paper. According to the characteristics of source coding and channel coding, this method uses redundancy in source coding to resist channel errors. In addition, the unequal error protection mechanism is added, which can improve transmission efficiency by adopting the code with good error correction ability for important code streams and adopting the code with poor error correction ability or not for non-important code streams according to the different importance of the output code streams of the source. In this way, a joint encoder responsible for both source compression and channel error protection is realized.

The security of enterprise users' personal privacy information and business sensitive data is the top priority of enterprise information security. This paper analyzed the necessity of data desensitization technology research from the perspective of the severity of sensitive data leakage, expounded the current research status of structured data desensitization from three aspects, such as domestic, foreign and power industry, summarized the classification and technology of structured data desensitization and analyzed their advantages and disadvantages. At the same time, the SQL statement parsing method was analyzed and studied. Finally, the future research direction of structured data desensitization and SQL statement parsing was prospected.

Wireless backhauling with renewable powered base stations (BSs) provides an attractive and cost-effective solution to enabling ultra dense cellular networks to meet the ever-increasing traffic demands of massive Internet of Things applications. To address the spectrum shortage in wireless backhaul networks, we propose to offload the delay-tolerant data traffic to the shared spectrum bands and jointly consider BSs' energy consumption, spectrum allocation, and data routing to maximize the amount of delivered data. Numerical results demonstrate that the adopted sequential fixing algorithm implements a near-optimal solution and our scheduling strategy significantly outperforms the conventional strategies which only considers spectrum allocation. Moreover, the impacts of resources availability on the performance of obtained strategies are analyzed.

The low-speed fieldbus is wildly applied to communicate and manage between or even within sub-devices in smart equipment. The typical low-cost communication protocols I²C, SPI, and UART are still playing important roles in different industries. A comparative analysis of the three communication is introduced in this paper, while the fault-tolerant design methods of I²C and SPI are described in detail. The communication can be recovered automatically from the error once the recommended firmware solutions can be adopted. Different situations in real life may lead to the use of low-speed communication technologies.

Coal resources are one of the vital energy sources in China. To ensure the safety of mine tunnels, drilling anchor robots are widely used in the task of anchor net support of mine walls. Since the anchor drilling robot needs to accurately identify and locate anchor holes and then install support anchors under harsh conditions such as dust and water mist, it needs to obtain clear images of the underground. This paper designed a forward-perceptive neural network model based on a convolutional neural network and attention-based layer structure to solve this problem. The model enhances the noise reduction capability of classical neural networks by forward perceptive design and uses pixel-level perceptual neurons to ensure that the distribution of effective pixel points is not affected during the denoising process. Finally, the model is tested to process the noisy underground mine images collected in the field. The signal-to-noise ratio of the processed images reaches more than 30, which exceeds the traditional noise reduction method by 1.3 times. Meanwhile, the pixel distribution of the processed images is not distorted.

Most of the traditional digital copyright management platforms are centralized, which is easy to cause the leakage of private data, that the rights of copyright owners are damaged. An image trading system is implemented using decentralized blockchain technology in this paper. The distributed storage of images is implemented using interplanetary file system (IPFS), the hash address returned by IPFS is stored in the blockchain, which solves the blockchain problems of difficult to capacity expansion and expensive storage space. The private data of images are encrypted storage to ensure the access security of data information. Unique verification of images to ensure that the same image cannot be registered repeatedly. Tests show that the system has good performance and security.

Speaker embedding based on neural network has achieved significant performance in speaker verification. However, this method results maladaptation of far-field speech. To fix this problem, this paper proposes applying environmental domain embedding to far-field speaker verification. By mapping speech environment to domain embedding and combining it with speaker features, the model is guided to detect the information of environment and speaker identity, thus improves the performance of far-field recognition. Comparing to traditional single domain and single embedding depth method, we explore the effects of domain type and embedding depth from gender, speech recording distance and noise. Furthermore, embeddings are applied to convolution through reshaping rather than vanilla fully-connected layers. The improved embedding method allows better combination. Experiments show the EER (Equal Error Rate) of gender-based, distance-based and noise-based model, comparing to baseline (ResNet-34, EER=1.28%), respectively decrease to 1.12%, 0.92% and 0.88%. And further comparison tests discover that carrying out combination in the second or third model stage achieves better performance.

The spread spectrum communication technology has been already widely used especially in the military filed because of its advantages such as high communication security, anti-interference capability, high utilization of spectrum and low interference to other communication systems etc. Direct sequence spread spectrum is one of the common methods to spread the spectrum and is used in satellite communication, location, secret communication and navigation etc. So it is meaningful to analyze the basic principle and characteristics of DSSS system.

In this paper, the research background is firstly given, then there is a detailed theoretical analysis on the principle and characteristics of the DSSS system. After a detailed discussion on DSSS system, the DSSS system is compared with another commonly used technology-FHSS. The advantages and disadvantages of DSSS compared with the frequency hopping system are summarized and the development of the technology in the future has been prospected.

Friendly docking various supermarket cashier systems with the WeChat payment system (WPS) achieve WPS at a supermarket. In addition to DLL (Dynamic Link Library) modules developed by multi-threading technology, it adopts hook and module injection to be introduced into and captures messages in the process message queue of the host program. Via matching the message strings with the bar code rules of WeChat, if satisfied, WPS will be switched to the client-side of the system and in-focus. After processing WeChat payment, WPS runs in the backend. Otherwise, the messages would be delayed and transmitted into the cashier system. WPS injects DLL files into the process of the Cashier system to achieve the docking functionality. This design scheme implements the docking ideally between two systems and supports all kinds of cashier systems with a positive user experience. Six hundred thousand commercial tenants are employing WPS in the market.

Ship navigation and communication are common application scenarios of maritime satellite, which have the advantages of high radiation efficiency, but often have high cost and need a lot of imported materials. Compared with linearly polarized antennas, circularly polarized antennas can receive signals from all directions efficiently. Therefore, it is feasible to design maritime satellite antenna based on circular polarization antenna principle. This paper introduces the basic principle of circular polarization antenna, based on which the design of maritime satellite patch antenna is studied, it is also the continuous direction of the research and design of maritime satellite antenna to make the antenna specifications more delicate and performance more perfect.

Midori is a low energy block cipher proposed at ASIACRYPT 2015. It is composed of two variants: Midori64 and Midori128, with block sizes equal to 64 and 128 bits respectively. These two ciphers provide the function of both encryption and decryption for security communication and storage with minimal area and energy overhead. In this paper, the immunity of Midori64 against integral fault attacks is investigated. Based on integral properties of Midori64 and fault model, a method to broken this cipher is presented. By injecting random faults in the 5^th round backwards in the encryption process, the faulty ciphertexts are obtained. Based on 4-round integral distinguisher of Midori64, the last round key is recovered by flittering the wrong key values. The experimental simulation results show that the key candidate values can be reduced from 2⁶⁴ to 2^31.2 by 16 sets faults injection. Using the last round key to decrypt the ciphertexts by one round, a same method can be used to recover the penultimate round key and thus the master key can be recovered by using the key expansion algorithm.

Based on the classical theory of atmospheric turbulence, the time-frequency transfer model of laser in free space channel was established, and the variation rules of turbulence intensity, outer scale, wind speed and transmission distance on the stabiliThe random fluctuation of atmospheric refractive index seriously affects the accuracy and stability ty of frequency transfer were simulated and compared with common noise types. The results show that the stability of frequency transfer decreases linearly with the increase of turbulence intensity and transmission distance. The interference of medium intensity turbulence to frequency signal is greater than the inherent noise of the system. The frequency stability is negatively correlated with the wind speed. Increasing the integration time may reduce the wind speed disturbance. This paper can provide reference for the design and optimization of laser time-frequency transmission system.

Scene images are rich in semantics, so scene classification is a very valuable and challenging task. Deep learning methods represented by DCNN have quickly become research hotspots with high efficiency and high accuracy. In response to the problem of improving classification performance, a network model that optimizes initial weights has appeared since 2018. However, none of the existing methods takes into account the adverse effect of the low quality of network input images on the classification accuracy. This article will combine super-resolution reconstruction technology to add image preprocessing operations before the classification network. The specific performance is the introduction of the SRCNN reconstruction model on the basis of the DCNN-ELM classification network. The following four experiments are implemented for three types of data sets: 1. Taking MIT Indoor and LSP as the research objects, compare the classification performance of the six existing methods to verify the multi-applicability of the optimized classification model. 2. Take five types of news scene sets as the research object to test the robustness of the optimized classification model. 3. Select three types of news scenes as the research objects, and divide them according to the degree of semantic clarity. The feasibility of the classification method proposed in this paper is discussed through the classification comparison experiment of whether the reconstruction preprocessing is performed on the input image. 4. Taking the atlas collected under severe weather as the research object, the application potential of the method in this paper is illustrated through the abovementioned comparative experiments.

In recent years, as a new generation of communication technology, 5G has played a major role in promoting economic and social development. At the same time, the Industrial Internet is the product of networking in the industrial field. Therefore, 5G and Industrial Internet, as key technologies supporting the digital transformation of the economy and society, will promote the explosion of large-scale innovative applications and accelerate the arrival of the digital age. In this paper, the key technologies in 5G and Industrial Internet are investigated. First, the key technologies of 5G including NOMA, MIMO, MMW, Network Slicing, MEC are discussed. Then, the architecture of the Industrial Internet, including super mobile computing terminals, software-defined machines is introduced. On this basis, the combination of 5G and Industrial Internet are studied, which provides help for further promoting the development of 5G network technology and Industrial Internet.

SM2 is a public key cryptosystem based on elliptic curve. Because of its high security and difficulty to crack, it is adopted by the national commercial cryptosystem to replace RSA. This paper studies and implements the encryption and decryption algorithm process based on SM2. Users can generate key pairs using the system, and quickly encrypt various types of files and generate cipher files. And then decrypt the encrypted files with public keys. The whole encryption and decryption process is displayed with a convenient interface, and various operation processes are recorded in logs for easy inspection. Finally, the test results of encrypting and decrypting different data files are displayed.

With the wide application of Unmanned Aerial Vehicle (UAV) cluster in military and civil applications, the security of network communication of UAV cluster has been paid more attention and become a key research field. Key management technology is the critical factor in ensuring the security of the cluster network. This paper analyzes the security of UAV cluster communication based on key management technology. Firstly, this paper classifies UAV according to its use and designs the UAV cluster network communication model. Then, this paper discusses the security threat of UAV cluster network communication. Finally, this paper refined the key management process of UAV cluster, designed the logical architecture of UAV cluster key management, and analyzed the key management technology, to facilitate the subsequent design of specific UAV cluster key management scheme and ensure its communications security.

Domain Name System (DNS) service is a core service on the Internet and a key link to ensure the normal operation of the Internet. Since DNS can often pass through firewalls without being intercepted, it creates favorable conditions for attackers to build a covert channel based on the DNS protocol. DNS over HTTPS (DoH) can encrypt DNS lookup and response data packets to ensure that data packets are not monitored and used, but it also makes the DNS tunnels more difficult to detect. Due to the security of DoH, researchers began trying to detect DoH tunnels by using machine or deep learning. The effect of the model is not good if the data quality is poor or the amount of data is insufficient. Due to the privacy of traffic data, it is usually difficult to collect and share private traffic data to a centralized server. We propose a federated-learning DoH traffic classification framework (FL_DoH_CF), which permits multiple institutions to detect DoH tunnels by using convolutional neural network (CNN) without sharing traffic data. The experiments demonstrate that FL_DoH_CF is competitive with centralized learning, and it is still robust for non-independent and identically distributed (No_IID) data, and even achieves an accuracy of 99.86% for extreme one-class No_IID data.

The status of national top-level nodes in the industrial internet are similar to the roots of the Domain Name System (DNS) of the Internet. With the increasingly mature development of the industrial Internet, the resolution pressure borne by national top-level nodes will be far greater than that of the root in DNS. It is very important for the identification and parsing system of industrial Internet to choose the appropriate deployment location for the national top-level node. This paper puts forward a set of research theory and model to determine the deployment position of national top-level node, which can be used to guide the construction of national top-level nodes of industrial Internet in the future.

Based on the analysis of the SIM card based Internet of Things security service, this paper proposes a secure communication module solution based on SIM card as root of trust. The solution can provide Internet of Things users a transparent policy management, transparent secure communication service which can help device users to realize security service transparently. The solution provides Internet of Things users with a more intelligent, more convenient management of secure communication.

This paper studies the password-based strong authentication key exchange protocol in the cross-domain scenario, and gives an end-to-end highly secure C2C-PAKE protocol, in which the server shares a password with the client and also has a pair of private/public keys. In contrast, the client has the shared password and the server's public-key. Specifically, we combine the ECQV implicit certificate scheme with the ECDH key exchange protocol to give a highly secure cross-domain protocol. Considering the application of the ECQV implicit certificate scheme in the Internet of Things (IoT), we give an analysis the possibility of the proposed protocol applied in the cross-domain scenario of the IoT. Finally, based on some common attacks, we show a security analysis of our protocol, which achieves perfect forward security and resists dictionary attacks, KCI attacks, replay attacks, Insider-Assisted attacks, and so on.

The continuous development of cyber security industry has brought about a new change in employment requirements, and the job requirements for the ability of employees show the characteristics of "customized" professional skills. The existing cyber security training effectiveness assessment method mainly from the point of view of task completion, based on the assessment results of task training effectiveness to subjectively evaluate the training personnel's ability, lack of training personnel in different aspects of professional skills and objective evaluation of the training personnel's ability, resulting in the assessment results can not determine whether the training personnel's ability can match the needs of the job. In response to the above problems, we propose a quantitative method of the trainee's ability based on the cyber security skills framework, which achieves a quantitative assessment of the trainees' abilities in cyber security training from the perspective of cyber security skills. The experimental results show that compared with the evaluation method of focusing on the completion of training tasks, the method proposed in this paper can realize the objective quantification of the trainee's ability, and provide the information of the trainee's mastery of different skills, and provide reference for guiding the cyber security employment.

Murals are China's precious historical and cultural heritage. They not only record the five thousand years of history of Chinese civilization, but also demonstrate the artistic creativity of the working people in ancient China. They have extremely high artistic and academic value. However, due to long-term exposure to wind and sun and man-made damage, some of the existing color murals have peeled off, color fading and other problems. In order to preserve these cultural treasures for a long time, many scholars have taken the digital restoration and protection of murals as their research direction. I regard the murals as pictures and this article will focus on the remedy of the mural color and the enhancement of the overall color, so that it can restore the original style. In the age of machine learning, I strongly recommend using machine learning for color restoration and restoration of color murals. However, unlike the traditional restoration of image color, the restoration of mural color is very complicated, and many factors need to be considered. Therefore, after referring to the method of the paper [1] of the City University of Hong Kong and Microsoft Research Asia, I optimized it and proposed a dual-domain translation network that uses real and intact color mural data and the data of various damaged color-fading murals. I train two variational autoencoders to convert them into two latent spaces respectively. The difference here with their method [1] is that the conversion between the two latent spaces is learned from real color murals and faded murals. This kind of translation has strong adaptability and integrity when used to restore the color of the mural. At the same time, in order to solve the fading problem of a mural caused by various reasons, I also referred to the methods of City University of Hong Kong and Microsoft Research Asia [1] to design a branch of global information extraction containing non-local modules. This branch is related to the fusion of latent spaces which can further improve the color recovery ability of murals that have faded due to various reasons. Especially from the results obtained later, the overall effect of this method is slightly better than other methods.

The identification information of the IoT device is an important content for the device configuration and management. In terms of identification carrier technology, compared to passive identification technologies such as barcodes and QR codes, security chips and modules as active identification carriers can actively initiate a connection to the platform side, without the need for identification reading and writing equipment to trigger, which is more automated and intelligent. However, the cost of the security chip is high, and the security level of the module is relatively low. Both of them need to be equipped with special software to realize the management ability of the identification. Neither of the two solutions can fully meet the needs of the IoT terminal. The IoT SIM card has the advantages of low cost, high universality, high security, two-way communication capabilities, etc., and can realize the function of active identification carrier. Therefore, this paper proposes a SIM card-based IoT identification management scheme. Through the management platform and the identification card application and other modules can manage the identification of IoT equipment, and at the same time, combined with the IoT security service system, the safety protection of identification is realized. This method is based on the BIP channel and does not require much management cost. It is a solution with high security, low cost and better convenience, and has a good application prospect in the field of equipment management.

The operation control system (OCS) is the main means to improve the observability and controllability of the virtual power plant (VPP). The risk assessment and control of VPP-OCS is the main aspect of the comprehensive safety risk management of the enterprise. Based on the perspective of COBIT 5 for risk, using its method and framework, this paper designs and implements the risk management and control process of VPP-OCS. In the implementation process, the identification of important assets, risk identification and disposal are carried out, and finally the risk control measures are listed. The research of this paper will play an important role in promoting the construction of safety production risk management system of VPP.

NIST launched the CAESAR competition for the world in 2014, followed by the current LWC competition. The design and security analysis on authenticated encryption (AE) algorithm have aroused a great upsurge in the world, aiming to meet the needs of the confidentiality and authenticity of the information. At the same time, all kinds of ingenious design and the corresponding security analysis emerged in endlessly. Although the SCREAM algorithm failed to be selected in the third round of CAESAR competition, based on its unique design, it still has a good reference and research significance to carry out safety analysis. In this thesis, according to the relative independence of each module of the SCREAM algorithm structure, the Chosen-message forgery attack of SCREAM was proposed under the condition of one or more pairs of ciphertext, so as to forge the ciphertext and generate the correct authentication tag to pass the verification. Meanwhile, the effectiveness of this method was proved, and the corresponding probability of success was provided. Compared with the existing analysis results, this method has better operability and universality.

Information system has become the main target of network attack in various fields. The attack on information system has become an important event that damages national security, political stability, economic lifeline and citizen security. Risk factor is the weak link in the information system that may be threatened to cause damage, and once it is successfully used, it may cause damage to assets. Although the existing research on vulnerability management and scientific and standardized risk assessment is relatively perfect, the scope is not enough to support and cover the assessment of information system risk factors. In this paper, the alarm data of an information system for half a year are analyzed and studied, and a standard method for risk factor analysis of information system is proposed, which can provide an important reference for risk factor classification of information system in finance, public communication and energy industries.

With the rapid progress of science and technology, information and communication technology began to come into people's lives. As one of the branches of information and communication technology, radio frequency identification (RFID) technology gradually extended to all areas of life. This paper focuses on the application of RFID in libraries digital information security and expounds the advantages of radio frequency identification technology in library information security technology. Comparing it with the traditional bar code identification technology, RFID technology performs better in library information management. After installing the RFID system, it is easier for the library to store and integrate book information. At present, most library RFID systems use two operating frequencies: high frequency and ultra-high frequency. This paper analyzes the performance differences between HF RFID and UHF RFID in library scenarios. The purpose of the comparison is to hope that libraries can choose and install appropriate identification frequencies according to their own needs. At this stage, RFID technology is superior to barcode technology in many aspects, but if it can solve the problems of cost, safety, and anti-interference ability, more and more libraries will be willing to introduce this technology.

Because the security situation of network environment is affected by many factors, the composition characteristics are relatively complex, resulting in a low rate of intrusion detection. Therefore, a research on intrusion detection method for complex network environment based on security situation awareness is proposed. By constructing the sparse self encoder and improving the penalty factor, the network features under the security situation are extracted under the layer by layer greedy training strategy, the genetic algorithm and ant colony algorithm are integrated to plan the optimal detection path, and the extracted feature results are used as the judgment standard to realize the intrusion detection of the network environment. The test results show that the detection rate of the proposed method for four kinds of intrusion data can reach more than 95%, and the average false positive rate is no more than 1.00%. Obviously, due to the comparison method, it has a good detection effect.

In order to solve the problem of high latency of the SDN fat tree structure in the unit test private network scenario. the adaptability of the Dijkstra algorithm is studied, and the algorithm is combined with OpenFlow flow entries. According to the real-time network global basic information provided by the controller, the data transmission rate of the source node and the destination node in the link is calculated. Recalculate routing paths for newly arrived data streams, allocate flow table resources, and ensure efficient and fast matching and forwarding of link layer data streams. The Mininet simulation platform is used to complete the experimental verification of the proposed method. The results show that this method can optimally plan the data transmission path, and the point-to-point transmission delay of the network can be reduced by more than half compared with before the improvement.

Mobile edge computing (MEC) is an emerging paradigm to meet the growing computing needs from mobile applications. By offloading computing intensive mobile application tasks to MEC server, users can experience low latency, low energy consumption and high reliability services. However, due to the limited battery capacity on the device, task execution will be interrupted when the battery energy is exhausted. To solve the above problems, a task offloading strategy based on energy aware is proposed. In the scenario of multi-user and single server, the total queuing cost in the process of mobile device requesting service is analyzed by using queuing theory. Then, the mathematical model of the scene is established, and the linear weighting and objective function of time delay and energy consumption of mobile device are designed. In order to optimize the objective function and obtain the optimal solution, an improved genetic algorithm (Ga Energy Aware, GEA) is proposed. Simulation results show that the algorithm can effectively reduce the power consumption and task execution delay of mobile device.