This PDF file contains the front matter associated with SPIE Proceedings Volume 12503 including the Title Page, Copyright information, Table of Contents, and Conference Committee Page.

In view of the fact that the current identity authentication protocol of the Internet of Vehicles has insufficient authentication strength and low security and leads to the leakage of user identity privacy. In this regard, this thesis mainly proposes a two-way authentication key exchange protocol based on smart cards, which uses the semigroup property of extended Chebyshev polynomials for session key negotiation, which not only realizes the two-way authentication of vehicles, roadside units and trusted institutions, but also the key agreement between the vehicle and the trusted authority is also completed. Using the BPR security model to prove the security, the results show that the protocol can meet the strong authentication requirements of 5G Internet of Vehicles and can effectively resist various security attacks, while protecting the user's identity privacy.

The application of emerging technologies has deepened the dependence on communication networks, and the surge of access equipment and bearer services has increased the difficulty of communication network monitoring, fault diagnosis and emergency disposal. Under the hybrid technical architecture and dual mode of operation and maintenance, comprehensively comb the experience, processes and methods of various network operation and maintenance lines, form a multi-agent decision-making model system that cooperates with each other, and combine immersive interactive auxiliary operation and maintenance means to effectively cope with massive operation and maintenance scenarios and tasks, and improve the communication network planning, business support, fault emergency response and prediction capabilities.

In this paper, a microservice-based satellite status monitoring system architecture is proposed, which can adapt to the increasingly complex business requirements of satellite status monitoring. The system completes satellite telemetry data processing and status monitoring through a group of low-coupling micro-services working together, thereby realizing rapid dynamic scaling and agile deployment of the system. The system realizes high-speed data storage based on data warehouse, realizes data cache and inter-component communication based on distributed data queue technology, and realizes service-oriented satellite status monitoring and fault diagnosis system by using micro-service component technology.

During the major epidemic, especially when the number of patients increases sharply and medical resources are tight, the prevention of the epidemic and the effective treatment of patients are extremely important. This paper comprehensively studies the services and applications of "AI+5G" technology in this context to improve the efficiency of therapy and prophylaxis and further expand the medical service space. Based on problems and practical applications, the role and characteristics of AI expertise and 5G IT in the anticipation and treatment of main pandemics are analyzed through case analysis and induction. The research results demonstrate that "5G plus AI" has great significance in initial discovery, monitoring and early warning, diagnosis assistance, pathological sharing and diagnosis and treatment development. The combination of AI technology and 5G network technology can effectively contribute to the reduction of cross-infection and treatment pressure, and effectively contribute to the improvement of treatment efficiency.

Fuzz testing technology is widely used in vulnerability mining. However, there is less research on fuzz testing specifically for libraries. Both public libraries and private libraries that come with commercial software may have security risks. In the past, testing a library required manual writing of drivers, which required testers to be familiar with the library and take a lot of time. Existing techniques mostly rely on static analysis of source code or focus on binary libraries which rely on header files, but no research in the case without header files and symbol information. In that case, there is faced with three major problems: 1) We do not have accurate information (such as the number and types of function parameters) about library functions. 2) The control and data dependencies among API functions are complex. 3) Effectively integrate the information to automatically generate a driver that can correctly perform fuzz testing. We propose AutoGenD to address the problem for libraries without source code, header files, and symbol information. First of all, we obtain the library function information based on the static analysis technology. Then, we obtain the data of the library function parameters through dynamic analysis technology. Finally, we take the template method to synthesize the driver. Through experiments, the driver generated by AutoGenD can effectively trigger existing vulnerabilities (11 bugs). The results indicate that it has the ability to automatically generate a fuzz driver.

This paper study a model for full duplex (FD) and half-duplex (HD) mode D2D underlaid cellular network system, and the position of users follows a homogeneous spatial Poisson point process (PPP). Toward this end, the network performance of FD and HD users underlaid cellular network is considered. we derive the approximate expressions of D2D coverage probability and sum rate by using the mathematical tools of stochastic geometry and analyze the effect of system parameters on network performance. The experiment results show that the sum rate performance and coverage probability of D2D (either FD-D2D or HD-D2D) can be effectively improved by changing system parameters. In addition, FD D2D does not always outperform the HD mode in terms of the coverage probability.

The paper studies on a novel erbium-doped fiber three-ring laser dynamic system as a chaotic or hyperchaotic laser emitter via “three coupled-rings” in depth. We prove theoretically the bi-stability and the twin-scroll strange attractor scenarios in the three-ring laser in mathematics, such as the dynamic behavior of the laser system may move around the two stable points to form the twin-scroll strange attractor of chaos under certain conditions. The evolution from a quasi-period state to chaos is further discussed via phase parametrization to exhibit a route to hyper chaos from period-doubling through a bifurcation scenario by varying one parameter value of the laser. Our numerical result indicates the infrequent existence of hyper chaos with five positive Lyapunov exponents, and which is a very rare. Finally, the quasi-periodic, chaotic and hyperchaotic regions are numerically demonstrated by the three positive Lyapunov exponents varying with the pump and gain. The fiber laser has an important value to study laser dynamic systems, secure communications, random signal generators, radars, and measuring devices.

Aiming at the problems of network intelligence requirements, single routing method and network service mismatch in the communication network of the power Internet of things. We propose a multi-modal routing technology based on a fully dimensionally definable platform to address different needs in various power businesses. The algorithm adopts a more reasonable routing method to provide differentiated services for different types of services based on a multi-modal network, which not only solves the problems of single routing mode and rigid structure existing in the current IP network, but also improves the service response speed and overall network performance. Simulation experiments show that the proposed algorithm increases the business success rate to a certain extent and maintains a good network congestion situation and a stable response speed in the case of a large amount of business.

The PUF-based VANET protocol has been widely studied for its ability to provide additional anti-counterfeiting features to devices. However, most PUF-based VANET protocols only protect the vehicle without considering the risk of copying secrets for the Roadside Unit (RSU), and do not consider the huge computational cost of error correction algorithms on embedded devices. Therefore, in this paper, we design a lightweight multi-PUF-based VANET protocol. In the proposed protocol, PUF is designed to be placed on both the vehicle and RSU to avoid the security threat posed by fake nodes, providing higher security of the system. Combined with the algorithms of reverse fuzzy extractor and fuzzy extractor, the computing resources can be reasonably allocated, and the delay can be reduced to the maximum extent. Experiments show that the proposed protocol has lower computational cost and higher security than the traditional PUF protocol.

This paper proposes an interference suppression method based on EEMD and ARIMA models, which decomposes the radar received signal by EEMD, then detects the interference signal, and then uses ARIMA models to recover the target echo signals. After simulation verification, the method can effectively suppress the interference.

The transmission of communication signals must not only ensure the speed of transmission, but also ensure the accuracy of transmission. The current research has been able to draw the advantages and disadvantages of the application of different error-correcting codes. However, it is not enough to simply compare transmission performance between different error correction code applications. Therefore, this paper uses MATLAB to simulate the transmission environment of the real channel, analyzes the reception error of short-length data through QPSK coding and IQ modulation, and analyzes the signal transmission using error correction code (Hamming code) and error detection code (even check code). The transmission performances were explored and compared, and their anti-noise interference performance was analyzed. Studies have shown that for short-length data, the error correction method using Hamming code can resist limited noise interference, but it can save bandwidth and increase the proportion of effective signal bits. In the low signal-to-noise ratio case, the method of using even-check code to detect errors has problems. Errors may be generated, and the proportion of effective signal bits is low, but the adaptability to noise is stronger than the previous method. Therefore, it is recommended to select different transmission methods for transmission environments with different signal-to-noise ratio ranges, and the combination of the two may further improve the anti-noise interference capability of signal transmission.

Network data security is very important for each user and service provider, and every process of network data transmission is at risk of being tampered with. In this paper, we proposed a bidirectional tampering method of network data for Linux system. In this method we use the combination of Netfilter and iptables, and use the NetfilterQueue module in Python to tamper with the packets entering and leaving a Linux host. This tampering operation is transparent to upper users and may cause domain name redirection or many other serious consequences which threaten the security of hosts. Finally, the feasibility of this tampering method is verified by experiments.

Docker has become the main service deployment mode of cloud platform because of its lightweight and portable characteristics. With the popularity of cloud services, the security of Docker becomes an increasingly concerned issue. On one hand, the security capability of Docker is insufficient currently. Docker is designed to reduce the attack surface mainly by simplifying the system functions. Meanwhile, there are only limited built-in security mechanisms for Docker which are incapable of controlling the runtime security for Docker. On the other hand, there lacks personalized security policy deployment and enforcement mechanism in Docker which cannot meet the special security needs of users for cloud services. Focusing on the above problems, this paper proposes a new security-on-demand framework for Docker called a “Policy-Customized Trusted Docker Architecture” (PC-TDA), which forces cloud service providers to provide secure Docker services satisfying the user customized security policies by introducing the attribute-based encryption mechanism. At the same time, PC-TDA uses the attribute-based signature mechanism to support the verification of the security status and security policies of cloud services. Based on the Kylin cloud, the PC-TDA is preliminarily implemented, and the verification results show the availability and rationality of our proposed architecture which can provide greater flexibility for cloud users to control Dockers.

Digital frequency spectrum analysis is widely used in signal processing and analyzing, which indicates the physical information and characteristics of components in a signal. According to the Nyquist sampling theorem, a sampling procedure is required to convert a continuous time signal into a discrete time signal, and the sampling rate should be more than twice the signal's maximum frequency. After sampling, windowing is used to truncate the signal and avoid spectrum leakage. Different windows are applied in different situations due to their different function expressions with different characteristics and they have different main lobe width and different side lobe levels. Discrete Fourier Transform (DFT) process is needed to sample the signal after windowing. N-point DFT means sampling X_w(e^jw) with the interval of 2 / π N but the larger N does not guarantee a better performance of DFT signal. Fast Fourier Transform (FFT) is a fast algorithm to calculate the DFT and often used in signal processing technology because of its obvious advantages of small computation and fast calculation. There are two typical functions in MATLAB which carry the FFT sequence with different functions. This paper introduces basic definitions of sampling and using Nyquist sampling theorem to determine sampling rate, compares different window functions and discusses how DFT length effect the frequency spectrum analysis.

The paper studies a novel laser network link and presents a novel erbium-doped fiber three-ring laser dynamic system as a network link emitter while an erbium-doped fiber dual-ring laser and an erbium-doped fiber single-ring laser are used as two nodes of the link. Then we introduce a physical optic model of a series synchronization network link guided by the three-ring laser and the link’s nodes with the dual-ring laser and the single-ring laser. We discuss how the emitter laser guides the dual-ring laser and the single-ring laser to obtain a series synchronization to form the series synchronization laser network link. In order to build the series synchronization network link, we use an anti-phase optic injection control technology to postulate the following ideas: the two rings of the three-ring laser obtain two synchronizations with the two rings of an erbium-doped fiber dual-ring laser, while the other ring acquires synchronization with another erbium-doped fiber single-ring laser. Such series synchronization network link is formed through optic connection and network technology. Moreover, a series synchronization network link is proven theoretically by two sets of synchronization equations. Our numerical results indicate the hyperchaotic series network synchronization. The proposed laser system and laser synchronization link can serve as a reference in the study of laser dynamic systems, secure communications, neural networks, and optical networks.

To address the problems of estimation errors of the Extended Kalman Filter (EKF) and the estimation errors of the gun position due to the long extrapolation distance, a ballistic extrapolation algorithm based on extended Kalman smoothing is proposed. The algorithm adopts a filtering model of seven-dimensional state vectors including the ballistic coefficients, takes the end point of the forward filtering as the start point of the smoothing, post-processes the filtered data by RTS smoothing algorithm, and then takes the midpoint of the smoothed data as the start point of the ballistic extrapolation, and uses the fourth order Runge-Kutta algorithm for ballistic extrapolation. The simulation results show that the extrapolation accuracy of the algorithm is significantly improved compared with the original algorithm.

The wide application of the Internet of Things promotes the accelerated deployment of IPv6. During the IPv4-IPv6 transition period, IPv4 and IPv6 will coexist for a long time, and the structure of IPv6 addresses is different from that of ordinary IPv6 addresses. This paper obtains the relevant domain names by performing reverse DNS query on the open source IPv6 address set, and then performs forward query according to the domain name to get the corresponding IPv4 address set. Based on the obtained dual-stack IPv4-IPv6 address pair datasets, firstly we did a statistical analysis of the number of addresses, then studied the correlation between IPv4-IPv6 address pairs and analyzed the structure characteristics of IPv6 address in dual-stack, which provides us with insights into IPv6 address detection.

This paper presents a robust 5G wireless networks visual safety positioning model, which combines CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) networks and can solve the sequence problem. Two parallel full connection layers are added to the output layer of the network to regress the RGB images to obtain the 3D position and 3D direction of the 5G wireless networks. Because the data set of each scene is small, the method of transfer learning is used in the training. The model has the best positioning result in the chess scene on the 7senses dataset, with the positioning error of 0.21m and 7.52°, and the positioning error in the seven scenes is 0.31m and 10.35°. This method can achieve good positioning effect in indoor positioning.

In order to realize the real-time requirement of BP neural network, this paper designs the hardware of BP neural network based on FPGA technology, in order to better play the performance of its neural network.

Aiming at the security deployment of network slicing, a security-aware network slicing deployment strategy (SANS-RL) is proposed. In the network slicing deployment stage, first perform virtual network function (VNF) mapping, and define the security constraints between VNFs and physical nodes from the perspective of security requirements; secondly, extract the security feature matrix of physical nodes in the process of slicing deployment, and use policy network the probability distribution of the security feature matrix is output and sorted; finally, the policy-based reinforcement learning method is used to solve the mapping result of the VNF. Comparative experimental results show that SANS-RL is superior to GRC and SVNE-RL algorithms in long-term benefit-to-cost ratio and request acceptance rate, and it ensures the security requirements of network slicing during deployment.

With the improvement of informatization and the wide application of artificial intelligence technology, the rapid development of Internet Finance and the development of technology have promoted the improvement of security and management of commercial banks. Based on the basic concepts of dynamic face recognition technology and quantum computing, combined with the technological development process and the application status of security prevention and control scenario, business handling scenario and personalized service scenario in bank personal business, this paper explores the feasibility of integrating quantum computing into the innovative practice of dynamic face recognition in view of the problems of dynamic face recognition. The paper mainly summarize the effectiveness of pattern innovation design and the problems that should be paid attention to the process setting. In conclusion, integrating quantum computing into dynamic facial recognition more effectively will be the direction for banks to explore personal business in the future.

With the development of electric vehicles, charging piles have been popularized. However, there are many problems in the security of charging piles in the market, especially information security accidents occur from time to time, so it is necessary to study the information security detection technology of charging piles. This paper studies the information security problem of electric vehicle charging pile and puts forward the information security test method of charging pile based on penetration test. It is verified that this method can effectively detect the information security problems of electric vehicle charging piles and provide an effective basis for the information security assessment of charging piles.

USIM Application Toolkit (USAT) is a command interaction mechanism between mobile Terminal and SIM card, SIM card applications can interact with users or terminals by calling USAT capability to realize various services. If these capabilities are improperly managed or not reasonably used and controlled, they may bring different levels of security risks. In addition to the hundreds of millions of user base of SIM cards, these security risks may cause extensive adverse effects. Based on the introduction of USAT capabilities, this paper has analyzed its typical security risks, then verified them one by one, and finally gave suggestions.

In recent years, with the development of the Internet of things industry, wireless sensor networks, as one of the most important technologies in its sensing layer, have been further studied and applied. However, the security threats it faces are becoming more and more diverse, and the security needs are becoming more and more urgent. However, wireless sensor network nodes are mutually exclusive, which leads to low accuracy of attack path prediction when malicious attacks occur. In order to ensure the security of wireless sensor networks, a hidden Markov based malicious attack path prediction method for wireless sensor networks is designed. The method of authentication coding is introduced to realize data integrity authentication. Extract the malicious software gene sequence, define the constraint conditions of the state transition probability matrix, build the attack means deduction model based on Hidden Markov, and realize the malicious attack path prediction. Experimental results: the average accuracy of the proposed method in four malicious attack scenarios is 73.583%, 55.674%, 32.923% and 85.014%, respectively. The results also verify that the proposed method improves the accuracy of malicious attack path prediction in wireless sensor networks based on Hidden Markov model and has good feasibility. It provides an important basis for wireless sensor network administrators to implement defense strategies in time.

With the continuous development of cloud computing, its security problems are frequently exposed. Since various attack methods are different, they will often face a wide variety of attack behaviors. Research on intrusion detection related technologies, mainly to solve malicious behaviors such as stealing, tampering, and hacking of data in the cloud environment, and its purpose is to intercept all kinds of attacks. An improved decision tree method for intrusion detection of host security vulnerabilities is proposed. Experiments show that this method has better detection performance.

Network traffic prediction is the key to network security management and improving network operation speed. This paper proposes a network traffic chaotic prediction method based on the improved beetle swarm algorithm and optimized support vector machine. Firstly, the new network traffic time series is obtained by the phase space reconstruction method. Then the SVM is optimized by the improved beetle swarm algorithm, and the optimized SVM is used to predict the chaos of the network traffic. Finally, the improved method is compared with the experimental results of network traffic chaos prediction based on particle swarm optimization support vector machine. The results show that the algorithm proposed in this paper has better results in terms of convergence effect and prediction accuracy.

With the rapid development of technologies such as cloud computing and Internet of Things, the amount of data outsourced to all kinds of remote cloud storage providers is growing. Out of security concerns, it is necessary for data owners to encrypt sensitive data before uploading it to the cloud, which makes it difficult to enforce access control of the outsourced data. Specifically, the time cost for user revocation is considerably high because of the large number of encryptions required for updating ciphertext in the cloud. In this paper, we utilize Proxy Re-Encryption (PRE) to propose PRE-DAC (PRE based Dynamic Access Control), an access control scheme for encrypted cloud data, where the efficiency of user revocation is significantly improved. Simulation results using real-world datasets show that the number of asymmetric encryptions for each revocation is prominently less than that of the existing schemes.

In software security, heap memory vulnerability is a common type of vulnerability. However, heap memory vulnerabilities can only lead to the collapse of the program in most cases. In order to achieve the purpose of getshell, it is still necessary to construct and use it. Zig language is a development-oriented programming language and will have more applications in the future. Based on the mechanism and principle of heap memory implementation in zig language, this paper finds several methods to exploit heap memory vulnerabilities, which can achieve the attack effect of writing eight-byte data at any writable address and applying for any address, respectively. On this basis, some corresponding defense methods are proposed, which provides ideas for the utilization and defense of heap memory vulnerabilities in binary security.

The Internet of Things has developed rapidly in recent years, and the concept of the Internet of Everything has been widely used. It is widely used in agriculture, medicine, industry and other fields. However, the application in the home field needs to be further promoted. This paper mainly introduces the application of Internet of Things in home furnishing. On the basis of existing applications, new technologies are added to meet the new needs of the society for home furnishing system. The current domestic market security system mostly stays in the simple access control security protection. As the number of people living alone increases gradually in recent years, people also have certain requirements for the safety detection of the family environment. This system in addition to a safer and faster way to unlock, but also to provide environmental safety detection, "Nine in one" environmental gas concentration detection and other intelligent security monitoring, so that users feel more assured.

Personalized recommendation system is divided into offline system and online system, the two are interconnected and cooperate with each other, and there are some problems in the development process. In response to the above problems, user-led recommendation and project-guided recommendation can be improved respectively. In addition, a good, personalized recommendation system is inseparable from excellent design. It is mainly divided into four management systems: source data management, ETL management, data policy management and cluster management, with the emphasis on data policy management. The research shows that the attack model and attack cost of fraud attack have different effects on the attack performance. In response to this problem, the performance of the collaborative filtering algorithm based on singular value segmentation SVD under different attack models is analyzed experimentally, and the impact of different filling scales and attack scales on attack efficiency is analyzed with three evaluation parameters.

Human perception technology has been utilized extensively in various fields. WiFi equipment has grown increasingly popular benefited from the rapid growth of wireless communication and the Internet of things, accompanied with an abundance of methods for person recognition based on WiFi channel state information (CSI). This study proposes a method of human body intelligent perception based on DCGAN to address the current issue of image blur generation in CSI-based imaging of the human body. The method mainly comprises the following steps: (i) obtaining CSI data for actual interior scenes, gathering scene video data based on time stamps, and creating heterogeneous data sets from the video extraction mask images collected; (ii) putting heterogeneous data sets into the DCGAN network for iterative training; (iii) obtaining a generation network for regenerating the environment mask image via the CSI data. Experiments demonstrate that such a method requires less space and generates a human body image more quickly, and that the IOU index of the generated image can be increased from 0.66 of the person-in-WiFi method to 0.696, allowing it to generate a clearer human body mask image than the prior network.

In order to improve the ability of identifying the abnormal traffic of 5G terminal cascaded wireless sensor networks, a method of identifying the abnormal traffic of 5G terminal cascaded wireless sensor networks based on impulse response feature detection is proposed. Construct the abnormal traffic data distribution structure model of the 5G terminal cascaded wireless sensor composite network, collect the big data and analyze the spectrum parameters of the 5G terminal cascaded wireless sensor composite network traffic by single frame scanning and mixed coding monitoring methods, extract the characteristic parameters of the sudden differential traffic data in the 5G terminal cascaded wireless sensor composite network, and analyze the data according to the traceability parameters of the abnormal traffic. The fuzzy clustering center tracking and detection of abnormal traffic data of 5G terminal cascade wireless sensor composite network is realized by adopting the traffic output conversion identification method of 5G terminal cascade wireless sensor composite network. According to the clustering result of fuzzy information, an abnormal feature distribution fusion model of abnormal traffic sequence of 5G terminal cascade wireless sensor composite network is established, and the impulse response characteristic value and association rule distribution set of abnormal traffic information are used for tracking and detection. The fuzzy weight analysis and adaptive learning of the abnormal traffic sequence of the 5G terminal cascade wireless sensor network are realized, and the abnormal traffic data in the 5G terminal cascade wireless sensor network is classified and marked by the BP neural network classification method, so as to realize the feature extraction and tracking detection of the abnormal traffic information of the 5G terminal cascade wireless sensor network. The test results show that this method has high accuracy, good tracking performance and strong anti-interference ability in tracking and detecting abnormal traffic data of 5G terminal cascaded wireless sensor integrated network, which improves the security and anti-attack ability of 5G terminal cascaded wireless sensor integrated network.

In order to solve the problems of patient privacy leakage, hospital secret leakage and "data island" between medical institutions caused by the centralized storage of medical records through third-party trust institutions. With the characteristics of block chain decentralization, tamper-proof and traceability, make full use of fabric support multi-channel advantage, build a block chain based medical information multi-channel storage mechanism, according to different types of medical information design multi-channel application framework for block chain underlying isolation, namely to ensure the integrity and security of patient data, the confidentiality of medical institutions drug information, and effectively solve the problem of "data island" between medical institutions.

The current cloud computing data security access control model reduces the risk of data being maliciously modified, but it can’t change the local attributes, and lacks some flexibility in the dynamic update of permissions. An improved CP-ABE algorithm is applied to design a cloud computing data security access control model. Between the cloud computing server and data users, a collaborative private key management mode is established based on the single attribute authority architecture. Each user has the attribute set and the corresponding private key. The CP-ABE algorithm cannot revoke the authority of a single user. A revocation scheme is added to the CP-ABE algorithm. The data belongs to the master, changes the hidden cloud key and authorized user attributes, and updates the private key with new random parameters. The improved CP-ABE algorithm is applied to establish the access control model. After the user accesses, the public key is obtained, and the input public and private keys are converted by the conversion algorithm. The test results show that the design model can reduce the time of key encryption and decryption when increasing the number of user attributes, complete data security access with less energy consumption, and is conducive to the expansion of access attributes.

To address the current problem of frequent piracy infringement of digital images, this paper proposes an image watermarking algorithm based on DWT and DCT. Firstly, the binary watermark is encrypted by the logistic chaos algorithm, which produces a chaotic sequence with better uncertainty and initial value sensitivity; secondly, the Arnold transform is applied to the encrypted watermarked image to eliminate the correlation between the watermarked pixels, so that the robustness of the image information is improved; the original image is decomposed by the second-level wavelet in the embedding process, and then the low-frequency sub-band LL1 is blocked In the embedding process, the original image is decomposed by the second-level wavelet, and then the low-frequency sub-band LL1 is chunked, and the pre-processed watermark information is embedded by modifying the IF coefficients through DCT transform. In this paper, Matlab simulation experiments are conducted for this algorithm, and the experimental results have good invisibility and good robustness when subjected to various malicious attacks, which is practical for safeguarding digital image copyright.