Biblio

A new round of scientific and technological revolution and industrial reform promote the intelligent development of automobile and promote the deep integration of automobile with Internet, big data, communication and other industries. At the same time, it also brings network and data security problems to automobile, which is very easy to cause national security and social security risks. Intelligent vehicle Ethernet intrusion detection can effectively alleviate the security risk of vehicle network, but the complex attack means and vehicle compatibility have not been effectively solved. This research takes the vehicle Ethernet as the research object, constructs the machine learning samples for neural network, applies the self coding network technology combined with the original characteristics to the network intrusion detection algorithm, and studies a self-learning vehicle Ethernet intrusion detection algorithm. Through the application and test of vehicle terminal, the algorithm generated in this study can be used for vehicle terminal with Ethernet communication function, and can effectively resist 34 kinds of network attacks in four categories. This method effectively improves the network security defense capability of vehicle Ethernet, provides technical support for the network security of intelligent vehicles, and can be widely used in mass-produced intelligent vehicles with Ethernet.

Device-to-Device (D2D) communications play a key role in the mobile communication networks. In spite of its benefits, new system architecture expose the D2D communications to unique security threats. Due to D2D users share the same licensed spectrum resources with the cellular users, both the cellular user and D2D receiver can eavesdrop each other's critical information. Thus, to maximize the secrecy rate from the perspective of physical layer security, the letter proposed a optimal power allocation scheme and subsequently to optimization problem of resource allocation is systematically investigated. The efficacy of the proposed scheme is assessed numerically.

The 5G technology brings the substantial improvement on the quality of services (QoS), such as higher throughput, lower latency, more stable signal and more ultra-reliable data transmission, triggering a revolution for the wireless mobile network. But in a general traffic channel in the 5G-based wireless mobile network, an attacker can detect a message transmitted over a channel, or even worse, forge or tamper with the message. Building a secure channel over the two parties is a feasible solution to this uttermost data transmission security challenge in 5G-based wireless mobile network. However, how to authentication the identities of the both parties before establishing the secure channel to fully ensure the data confidentiality and integrity during the data transmission has still been a open issue. To establish a fully secure channel, in this paper, we propose a strongly secure pairing-free certificateless authenticated key agreement (PF-CL-AKA) protocol with two-way identity-based authentication before extracting the secure session key. Our protocol is provably secure in the Lippold model, which means our protocol is still secure as long as each party of the channel has at least one uncompromised partial private term. Finally, By the theoretical analysis and simulation experiments, we can observe that our scheme is practical for the real-world applications in the 5G-based wireless mobile network.

In the era of big data, more and more users store data in the cloud. Massive amounts of data have brought huge storage costs to cloud storage providers, and data deduplication technology has emerged. In order to protect the confidentiality of user data, user data should be encrypted and stored in the cloud. Therefore, deduplication of encrypted data has become a research hotspot. Cloud storage provides users with data sharing services, and the sharing of encrypted data is another research hotspot. The combination of encrypted data deduplication and sharing will inevitably become a future trend. The current better-performing updateable block-level message-locked encryption (UMLE) deduplication scheme does not support data sharing, and the performance of the encrypted data de-duplication scheme that introduces data sharing is not as good as that of UMLE. This paper introduces the ciphertext policy attribute based encryption (CP-ABE) system sharing mechanism on the basis of UMLE, applies the CP-ABE method to encrypt the master key generated by UMLE, to achieve secure and efficient data deduplication and sharing. In this paper, we propose a permission verification method based on bilinear mapping, and according to the definition of the security model proposed in the security analysis phase, we prove this permission verification method, showing that our scheme is secure. The comparison of theoretical analysis and simulation experiment results shows that this scheme has more complete functions and better performance than existing schemes, and the proposed authorization verification method is also secure.

Based on the structure of turbo-polar codes, a secure symmetric encryption scheme is proposed to enhance information transmission security in this paper. This scheme utilizes interleaving at information bits and puncturing at parity bits for several times in the encoder. Correspondingly, we need to do the converse interleaving and fill zeros accurately at punctured position. The way of interleaving and puncturing is controlled by the private key of symmetric encryption, making sure the security of the system. The security of Secure Turbo-Polar Codes (STPC) is analyzed at the end of this paper. Simulation results are given to shown that the performance and complexity of Turbo-Polar Codes have little change after symmetric encryption. We also investigate in depth the influence of different remaining parity bit ratios on Frame Error Rate (FER). At low Signal to Noise Rate (SNR), we find it have about 0.6dB advantage when remaining parity bit ratio is between 1/20 and 1/4.

Subscription permanent identifier has been concealed in the 5G systems by using the asymmetric encryption scheme as specified in standard 3GPP TS 33.501 to protect the subscriber privacy. The standardized scheme is however subject to the SUPI guess attack as the public key of the home network is publicly available. Moreover, it lacks the inherent mechanism to prevent SUCI replay attacks. In this paper, we propose three methods to enhance the security of the 3GPP scheme to thwart the SUPI guess attack and replay attack. One of these methods is suggested to be used to strengthen the security of the current subscriber protection scheme.

The semaphore mechanism is an important part of the embedded operating system. Therefore, it is very necessary to ensure its safety. Traditional software testing methods are difficult to ensure 100% coverage of the program. Therefore, it is necessary to adopt a formal verfication method which proves the correctness of the program theoretically. This paper proposes a proof framework based on the theorem proof tool Coq: modeling the semaphore mechanism, extracting important properties from the requirement documents, and finally verifying that the semaphore mechanism can meet these properties, which means the correctness of the semaphore mechanism is proved and also illustrates the feasibility of the verification framework proposed in this paper, which lays a foundation for the verification of other modules of operating systems.

As a large amount of renewable energy is injected into the power grid, the source side of the power grid becomes extremely uncertain. Traditional static safety analysis methods based on pure physical models can no longer quickly and reliably give analysis results. Therefore, this paper proposes a deep learning-based static security analytical method. First, the static security assessment index of the power grid under the N-1 principle is proposed. Secondly, a neural network model and its input and output data for static safety analysis problems are designed. Finally, the validity of the proposed method was verified by IEEE grid data. Experiments show that the proposed method can quickly and accurately give the static security analysis results of the source-side high uncertainty grid.

Aimed at the high requirement of timeliness in the process of information assurance, this paper describes the average time delay of information transmission in the system, and designs a timeliness index that can quantitatively describe the ability of early warning information assurance. In response to the problem that system capability cannot meet operational requirements due to enemy attacks, this paper analyzes the structure of the early warning information system, Early warning information complex network model is established, based on the timeliness index, a genetic algorithm based on simulated annealing with special chromosome coding is proposed.the algorithm is used to adjust the network model structure, the ability of early warning information assurance has been improved. Finally, the simulation results show the effectiveness of the proposed method.

Focusing on security management for supply chain under emergencies, this paper analyzes the characteristics of supply chain risk, clarifies the relationship between business continuity management and security management for supply chain, organizational resilience and security management for supply chain separately, so as to propose suggestions to promote the realization of security management for supply chain combined these two concepts, which is of guiding significance for security management for supply chain and quality assurance of products and services under emergencies.

At present, in the face of the huge and complex data in cloud computing, the parallel computing ability of quantum computing is particularly important. Quantum principal component analysis algorithm is used as a method of quantum state tomography. We perform feature extraction on the eigenvalue matrix of the density matrix after feature decomposition to achieve dimensionality reduction, proposed quantum principal component extraction algorithm (QPCE). Compared with the classic algorithm, this algorithm achieves an exponential speedup under certain conditions. The specific realization of the quantum circuit is given. And considering the limited computing power of the client, we propose a quantum homomorphic ciphertext dimension reduction scheme (QHEDR), the client can encrypt the quantum data and upload it to the cloud for computing. And through the quantum homomorphic encryption scheme to ensure security. After the calculation is completed, the client updates the key locally and decrypts the ciphertext result. We have implemented a quantum ciphertext dimensionality reduction scheme implemented in the quantum cloud, which does not require interaction and ensures safety. In addition, we have carried out experimental verification on the QPCE algorithm on IBM's real computing platform. Experimental results show that the algorithm can perform ciphertext dimension reduction safely and effectively.

This paper argues that the security management of the robot supply chain would preferably focus on Sino-US relations and technical bottlenecks based on a comprehensive security analysis through open-source intelligence and data mining of associated discourses. Through the lens of the newsboy model and game theory, this study reconstructs the risk appraisal model of the robot supply chain and rebalances the process of the Sino-US competition game, leading to the prediction of China's strategic movements under the supply risks. Ultimately, this paper offers a threefold suggestion: increasing the overall revenue through cost control and scaled expansion, resilience enhancement and risk prevention, and outreach of a third party's cooperation for confrontation capabilities reinforcement.

Internet of Things (IoT) apps provide great convenience but exposes us to new safety threats. Unlike traditional software systems, threats may emerge from the joint behavior of multiple apps. While prior studies use handcrafted safety and security policies to detect these threats, these policies may not anticipate all usages of the devices and apps in a smart home, causing false alarms. In this study, we propose to use the technique of mining sandboxes for securing an IoT environment. After a set of behaviors are analyzed from a bundle of apps and devices, a sandbox is deployed, which enforces that previously unseen behaviors are disallowed. Hence, the execution of malicious behavior, introduced from software updates or obscured through methods to hinder program analysis, is blocked.While sandbox mining techniques have been proposed for Android apps, we show and discuss why they are insufficient for detecting malicious behavior in a more complex IoT system. We prototype IoTBox to address these limitations. IoTBox explores behavior through a formal model of a smart home. In our empirical evaluation to detect malicious code changes, we find that IoTBox achieves substantially higher precision and recall compared to existing techniques for mining sandboxes.

With the development of deep learning and the progress of natural language processing technology, as well as the continuous disclosure of judicial data such as judicial documents, legal intelligence has gradually become a research hot spot. The crime classification task is an important branch of text classification, which can help people related to the law to improve their work efficiency. However, in the actual research, the sample data is small and the distribution of crime categories is not balanced. To solve these two problems, BERT was used as the encoder to solve the problem of small data volume, and attribute extraction network was added to solve the problem of unbalanced distribution. Finally, the accuracy of 90.35% on small sample data set could be achieved, and F1 value was 67.62, which was close to the best model performance under sufficient data. Finally, a text enhancement method based on back-translation technology is proposed. Different models are used to conduct experiments. Finally, it is found that LSTM model is improved to some extent, but BERT is not improved to some extent.

Surface acoustic wave devices based on LiNbO3/interlayer/substrate layered structure have attracted great attention due to the high electromechanical coupling coefficient (K2) of LiNbO3 and the energy confinement effect of the layered structure. In this study, 30° YX-LiNbO3 (LN)/SiO2/Si multilayered structure, which can excited shear-horizontal surface acoustic wave (SH-SAW) with high K2, was proposed. The optimized orientation of LiNbO3 was verified by the effective permittivity method based on the stiffness matrix. The phase velocity, K2 value, and temperature coefficient of frequency (TCF) of the SH-SAW were calculated as a function of the LiNbO3 thickness at different thicknesses of the SiO2 in 30° YX-LiNbO3/SiO2/Si multilayer structure by finite element method (FEM). The results show that the optimized LiNbO3 thickness is 0.1 and the optimized SiO2 thickness is 0.2λ. The optimized Al electrode thickness and metallization ratio are 0.07 and 0.4, respectively. The K2 of the SH-SAW is 29.89%, the corresponding phase velocity is 3624.00 m/s and TCF is about 10 ppm/°C with the optimized IDT/30° YX-LiNbO3/SiO2/Si layered structure.

The existing education system, which incorporates school assessments, has some flaws. Conventional teaching methods give students no immediate feedback, also make teachers to spend hours grading repetitive assignments, and aren't very constructive in showing students how to improve in their academics, and also fail to take advantage of digital opportunities that can improve learning outcomes. In addition, since a single teacher has to manage a class of students, it gets difficult to focus on each and every student in the class. Furthermore, with the help of a management system for better learning, educational organizations can now implement administrative analytics and execute new business intelligence using big data. This data visualization aids in the evaluation of teaching, management, and study success metrics. In this paper, there is put forward a discussion on how Data Mining and Data Analytics can help make the experience of learning and teaching both, easier and accountable. There will also be discussion on how the education organization has undergone numerous challenges in terms of effective and efficient teachings, student-performance. In addition development, and inadequate data storage, processing, and analysis will also be discussed. The research implements Python programming language on big education data. In addition, the research adopted an exploratory research design to identify the complexities and requirements of big data in the education field.

To meet the application need of dynamic visualization VR display of 3D time-varying field, this paper designed an immersive visualization VR system of 3D time-varying field based on the Unity 3D framework. To reduce visual confusion caused by 3D time-varying field flow line drawing and improve the quality and efficiency of visualization rendering drawing, deep learning was used to extract features from the mesoscale vortex of the 3D time-varying field. Moreover, the 3D flow line dynamic visualization drawing was implemented through the Unity Visual Effect Graph particle system.

Nowadays, smart contracts manage more and more digital assets and have become an attractive target for adversaries. To prevent smart contracts from malicious attacks, a thorough test is indispensable and must be finished before deployment because smart contracts cannot be modified after being deployed. Fuzzing is an important testing approach, but most existing smart contract fuzzers can hardly solve the constraints which involve deeply nested conditional statements, resulting in low coverage. To address this problem, we propose Targy, an efficient targeted mutation strategy based on dynamic taint analysis. We obtain the taint flow by dynamic taint propagation, and generate a more accurate mutation strategy for the input parameters of functions to simultaneously satisfy all conditional statements. We implemented Targy on sFuzz with 3.6 thousand smart contracts running on Ethereum. The numbers of covered branches and detected vulnerabilities increase by 6% and 7% respectively, and the average time required for covering a branch is reduced by 11 %.

MIMO technology has been widely used in the telecommunication systems nowadays, and the space-time coding is a key part of MIMO technology. A good coding scheme can exploit the spatial diversity to correct the error which is generated in transmission, and increase the normalized transfer rate with low decoding complexity. On the Basis of the research on different Space-Time Block Codes, this essay proposes a new STBC, Diagonal Block Orthogonal Space-Time Block Code. Then we will compare it with other STBCs in the performance of bit error rate, transfer rate, decoding complexity and peek-to-average power ratio, the final result will prove the superiority of DBOAST.

Mobile devices have been widely used to deploy security-sensitive applications such as mobile payments, mobile offices etc. SM2 digital signature technology is critical in these applications to provide the protection including identity authentication, data integrity, action non-repudiation. Since mobile devices are prone to being stolen or lost, several server-aided SM2 cooperative signature schemes have been proposed for the mobile scenario. However, existing solutions could not well fit the high-concurrency scenario which needs lightweight computation and communication complexity, especially for the server sides. In this paper, we propose a SM2 cooperative signature algorithm (SM2-CSA) for the high-concurrency scenario, which involves only one-time client-server interaction and one elliptic curve addition operation on the server side in the signing procedure. Theoretical analysis and practical tests shows that SM2-CSA can provide better computation and communication efficiency compared with existing schemes without compromising the security.

Aiming at the problem of low detection accuracy in traditional power system network intrusion detection methods, in order to improve the performance of power system network intrusion detection, a power system network intrusion detection method based on random forest algorithm is proposed. Firstly, the power system network intrusion sub sample is selected to construct the random forest decision tree. The random forest model is optimized by using the edge function. The accuracy of the vector is judged by the minimum state vector of the power system network, and the measurement residual of the power system network attack is calculated. Finally, the power system network intrusion data set is clustered by Gaussian mixture clustering Through the design of power system network intrusion detection process, the power system network intrusion detection is realized. The experimental results show that the power system network intrusion detection method based on random forest algorithm has high network intrusion detection performance.

With the rapid development of 5G, the Internet of Things (IoT) and edge computing technologies dramatically improve smart industries' efficiency, such as healthcare, smart agriculture, and smart city. IoT is a data-driven system in which many smart devices generate and collect a massive amount of user privacy data, which may be used to improve users' efficiency. However, these data tend to leak personal privacy when people send it to the Internet. Differential privacy (DP) provides a method for measuring privacy protection and a more flexible privacy protection algorithm. In this paper, we study an estimation problem and propose a new frequency estimation algorithm named MFEA that redesigns the publish process. The algorithm maps a finite data set to an integer range through a hash function, then initializes the data vector according to the mapped value and adds noise through the randomized response. The frequency of all interference data is estimated with maximum likelihood. Compared with the current traditional frequency estimation, our approach achieves better algorithm complexity and error control while satisfying differential privacy protection (LDP).

The construction of the power Internet of Things has led various terminals to access the corporate network on a large scale. The internal and external business interaction and data exchange are more extensive. The current security protection system is based on border isolation protection. This is difficult to meet the needs of the power Internet of Things connection and open shared services. This paper studies the application scheme of the ``zero trust'' typical business scenario of the power Internet of Things with ``Continuous Identity Authentication and Dynamic Access Control'' as the core, and designs the power internet security protection architecture based on zero trust.

With the rapid construction and popularization of smart grid, the security of data in smart grid has become the basis for the safe and stable operation of smart grid. This paper proposes a data security threat discovery model for smart grid. Based on the prediction data analysis method, combined with migration learning technology, it analyzes different data, uses data matching process to classify the losses, and accurately predicts the analysis results, finds the security risks in the data, and prevents the illegal acquisition of data. The reinforcement learning and training process of this method distinguish the effective authentication and illegal access to data.

Intelligent reflecting surfaces (IRS) can improve the physical layer security (PLS) by providing a controllable wireless environment. In this paper, we propose a novel PLS technique with the help of IRS implemented by an intelligent mirror array for the visible light communication (VLC) system. First, for the IRS aided VLC system containing an access point (AP), a legitimate user and an eavesdropper, the IRS channel gain and a lower bound of the achievable secrecy rate are derived. Further, to enhance the IRS channel gain of the legitimate user while restricting the IRS channel gain of the eavesdropper, we formulate an achievable secrecy rate maximization problem for the proposed IRS-aided PLS technique to find the optimal orientations of mirrors. Since the sensitivity of mirrors’ orientations on the IRS channel gain makes the optimization problem hard to solve, we transform the original problem into a reflected spot position optimization problem and solve it by a particle swarm optimization (PSO) algorithm. Our simulation results show that secrecy performance can be significantly improved by adding an IRS in a VLC system.