Biblio

In the context of Industrial Internet logo analysis, this paper analyzes the feasibility and outstanding advantages of the blockchain technology applied to supply chain data supervision combining the pain spots of traditional supply chain management system and the technical superiority. Although blockchain technology has uprooted some deep-entrenched problems of supply chain data management system, it brings new issues to government supervision in the meanwhile. Upon the analysis of current development and the new problems of blockchain-based supply chain data management system, a new parent-children blockchain-based supply chain data supervision system is proposed, which targets to overcome the dilemma faced by the governmental regulation of supply chain. Firstly, with the characteristics of blockchain including decentralization, non-tampering and non-repudiation, the system can solve the problem puzzling the traditional database about untruthful and unreliable data, and has advantages in managing supply chain and realizing product traceability. The authenticity and reliability of data on the chain also make it easier for the government to investigate and affix the responsibility of vicious incidents. At the same time, the system adopts the parent-children chain structure and the storage mode combining on-chain and off-chain resources to overcome the contradiction between information disclosure requirements of the government and privacy protection requirements of enterprises, which can better meet the needs of various users. Moreover, the application of smart contracts can replace a large number of the manual work like repetitive data analysis, which can make analysis results more accurate and avoid human failure.

Signcryption is a sophisticated cryptographic tool that combines the benefits of digital signature and data encryption in a single step, resulting in reduced computation and storage cost. However, the existing signcryption techniques do not account for a scenario in which a company must escrow an employee's private encryption key so that the corporation does not lose the capacity to decrypt a ciphertext when the employee or user is no longer available. To circumvent the issue of non-repudiation, the private signing key does not need to be escrowed. As a result, this paper presents an implicit certificate-based signcryption technique with private encryption key escrow, which can assist an organization in preventing the loss of private encryption. A certificate, or more broadly, a digital signature, protects users' public encryption and signature keys from man-in-the-middle attacks under our proposed approach.

As the rapid development of information technology and networks, there have been several new challenges to data security. For security needs in the process of data transmission and storage, the data security protection mechanism based on SM algorithm is studied. In addition, data cryptographic security protection system model composed of cryptographic infrastructure, cryptographic service nodes and cryptographic modules is proposed. As the core of the mechanism, SM algorithm not only brings about efficient data encryption and decryption, but ensures the security, integrity and non-repudiation of data transmission and storage. Secure and controllable key management is implemented by this model, which provides easy-to-expandable cryptographic services, and brings efficient cryptographic capabilities applicable for multiple scenarios.

Aiming at the fact that the existing feature quantities cannot well identify the magnetizing inrush current during remanence and bias and the huge number of feature quantities, a new identification method using empirical mode decomposition energy index and artificial intelligence algorithm is proposed in 'this paper. Decomposition and denoising are realized through empirical mode decomposition, and then the corresponding energy index is obtained for the waveform of each inherent modal component and simplified by the mean impact value method. Finally, the accuracy of prediction using artificial intelligence algorithm is close to 100%. This reflects the practicality of the method proposed in 'this article.

In recent years, there have been situations in which the zero-sequence protection of the transformer has been incorrectly operated due to the converter transformer energizing or fault recovery. For converter transformers, maloperation may also occur. However, there is almost no theoretical research on the zero-mode inrush currents of converter transformers. This paper studies the characteristics of the zero-mode inrush currents of the converter transformers, including the relationship between the amplitude and attenuation characteristics of the zero-mode inrush currents of converter transformers, and their relationship with the system resistance, remanence, and closing angle. First, based on the T-type equivalent circuit of the transformer, the equivalent circuit of the zero-mode inrush current of each transformer is obtained. On this basis, the amplitude relationship of the zero-mode inrush currents of different converter transformers is obtained: the zero-mode inrush current of the energizing pole YY transformer becomes larger than the YD transformer, the energized pole YD becomes greater than the YY transformer, and the YY transformer zero-mode inrush current rises from 0. It is also analyzed that the sympathetic interaction will make the attenuation of the converter transformer zero-mode inrush current slower. The system resistance mainly affects the initial attenuation speed, and the later attenuation speed is mainly determined by the converter transformer leakage reactance. Finally, PSCAD modeling and simulation are carried out to verify the accuracy of the theoretical analysis.

Human-machine teaming (HMT) operates in a context defined by the mission. Varying from the complexity and disturbance in the cooperation between humans and machines, a single machine has difficulty handling work with humans in the scales of efficiency and workload. Swarm of machines provides a more feasible solution in such a mission. Human-swarm teaming (HST) extends the concept of HMT in the mission, such as persistent surveillance, search-and-rescue, warfare. Bringing the concept of HST faces several scientific challenges. For example, the strategies of allocation on the high-level decision making. Here, human usually plays the supervisory or decision making role. Performance of such fixed structure of HST in actual mission operation could be affected by the supervisor’s status from many aspects, which could be considered in three general parts: workload, situational awareness, and trust towards the robot swarm teammate and mission performance. Besides, the complexity of a single human operator in accessing multiple machine agents increases the work burdens. An interface between swarm teammates and human operators to simplify the interaction process is desired in the HST.In this paper, instead of purely considering the workload of human teammates, we propose the computational model of human swarm interaction (HSI) in the simulated map surveillance mission. UAV swarm and human supervisor are both assigned in searching a predefined area of interest (AOI). The workload allocation of map monitoring is adjusted based on the status of the human worker and swarm teammate. Workload, situation awareness ability, trust are formulated as independent models, which affect each other. A communication-aware UAV swarm persistent surveillance algorithm is assigned in the swarm autonomy portion. With the different surveillance task loads, the swarm agent’s thrust parameter adjusts the autonomy level to fit the human operator’s needs. Reinforcement learning is applied in seeking the relative balance of workload in both human and swarm sides. Metrics such as mission accomplishment rate, human supervisor performance, mission performance of UAV swarm are evaluated in the end. The simulation results show that the algorithm could learn the human-machine trust interaction to seek the workload balance to reach better mission execution performance. This work inspires us to leverage a more comprehensive HST model in more practical HMT application scenarios.

Edge data caching has attracted tremendous attention in recent years. Service providers can consider caching data on nearby locations to provide service for their app users with relatively low latency. The key to enhance the user experience is appropriately choose to cache data on the suitable edge servers to achieve the service providers' objective, e.g., minimizing data retrieval latency and minimizing data caching cost, etc. However, Quality of Experience (QoE), which impacts service providers' caching benefit significantly, has not been adequately considered in existing studies of edge data caching. This is not a trivial issue because QoE and Quality-of-Service (QoS) are not correlated linearly. It significantly complicates the formulation of cost-effective edge data caching strategies under the caching budget, limiting the number of cache spaces to hire on edge servers. We consider this problem of QoE-aware edge data caching in this paper, intending to optimize users' overall QoE under the caching budget. We first build the optimization model and prove the NP-completeness about this problem. We propose a heuristic approach and prove its approximation ratio theoretically to solve the problem of large-scale scenarios efficiently. We have done extensive experiments to demonstrate that the MPSG algorithm we propose outperforms state-of-the-art approaches by at least 68.77%.

Model predictive control (MPC) has become one of the most attractive control techniques due to its outstanding dynamic performance for motor drives. Besides, MPC with constant switching frequency (CSF-MPC) maintains the advantages of MPC as well as constant frequency but the selection of weighting factors in the cost function is difficult for CSF-MPC. Fortunately, the application of artificial neural networks (ANN) can accelerate the selection without any additional computation burden. Therefore, this paper designs a specific artificial neural network optimized by genetic algorithm (GA-ANN) to select the optimal weighting factors of CSF-MPC for permanent magnet synchronous motor (PMSM) drives fed by three-level T-type inverter. The key performance metrics like THD and switching frequencies error (ferr) are extracted from simulation and this data are utilized to train and evaluate GA-ANN. The trained GA-ANN model can automatically and precisely select the optimal weighting factors for minimizing THD and ferr under different working conditions of PMSM. Furthermore, the experimental results demonstrate the validation of GA-ANN and robustness of optimal weighting factors under different torque loads. Accordingly, any arbitrary user-defined working conditions which combine THD and ferr can be defined and the optimum weighting factors can be fast and explicitly determined via the trained GA-ANN model.

In the federated learning system, parameter gradients are shared among participants and the central modulator, while the original data never leave their protected source domain. However, the gradient itself might carry enough information for precise inference of the original data. By reporting their parameter gradients to the central server, client datasets are exposed to inference attacks from adversaries. In this paper, we propose a quantitative metric based on mutual information for clients to evaluate the potential risk of information leakage in their gradients. Mutual information has received increasing attention in the machine learning and data mining community over the past few years. However, existing mutual information estimation methods cannot handle high-dimensional variables. In this paper, we propose a novel method to approximate the mutual information between the high-dimensional gradients and batched input data. Experimental results show that the proposed metric reliably reflect the extent of information leakage in federated learning. In addition, using the proposed metric, we investigate the influential factors of risk level. It is proven that, the risk of information leakage is related to the status of the task model, as well as the inherent data distribution.

The Internet of Things technology is one of the important directions of Smart Grid research, involving many wireless sensors and communication facilities, and has high requirements for security. The physical layer security technology can effectively solve the security problems under wireless communication. As the most common application scenario of wireless communication is multi-node wireless network communication, group secret key (GSK) based on physical layer security and information theory security is gradually attracting investigator’s interest. In this paper, a novel physical layer GSK generation scheme based on code-domain exchange of channel information in mesh network is proposed. Instead of traditional side-information exchange in symbol-domain, error-correcting code is applied to finish information exchange and reconciliation simultaneously in code-domain. Each node processes the known channel bit sequence and then encodes it to generate a check sequence. After broadcasting the check bit sequence to other nodes, each node decodes the received check bit sequences to obtained the unknown channel information. The simulation results show that the scheme can effectively reduce the times of information exchanges while keeping a good performance including low bit error rate and low block error rate.

The rapid development of machine learning technology has prompted the applications of Automatic Speech Recognition(ASR). However, studies have shown that the state-of-the-art ASR technologies are still vulnerable to various attacks, which undermines the stability of ASR destructively. In general, most of the existing attack techniques for the ASR model are based on white box scenarios, where the adversary uses adversarial samples to generate a substituted model corresponding to the target model. On the contrary, there are fewer attack schemes in the black-box scenario. Moreover, no scheme considers the problem of how to construct the architecture of the substituted models. In this paper, we point out that constructing a good substituted model architecture is crucial to the effectiveness of the attack, as it helps to generate a more sophisticated set of adversarial examples. We evaluate the performance of different substituted models by comprehensive experiments, and find that ensemble substituted models can achieve the optimal attack effect. The experiment shows that our approach performs attack over 80% success rate (2% improvement compared to the latest work) meanwhile maintaining the authenticity of the original sample well.

Robustness, both to accident and to malevolent perturbations, is a crucial determinant of the successful deployment of deep convolutional neural network based SAR ATR systems in various security-sensitive applications. This paper performs a detailed adversarial robustness evaluation of deep convolutional neural network based SAR ATR models across two public available SAR target recognition datasets. For each model, seven different adversarial perturbations, ranging from gradient based optimization to self-supervised feature distortion, are generated for each testing image. Besides adversarial average recognition accuracy, feature attribution techniques have also been adopted to analyze the feature diffusion effect of adversarial attacks, which promotes the understanding of vulnerability of deep learning models.

The traditional data storage method often adopts centralized architecture, which is prone to trust and security problems. This paper proposes a trusted data storage and access control scheme combining blockchain and attribute-based encryption, which allow cyber-physical system (CPS) nodes to realize the fine-grained access control strategy. At the same time, this paper combines the blockchain technology with distributed storage, and only store the access control policy and the data access address on the blockchain, which solves the storage bottleneck of blockchain system. Furthermore, this paper proposes a novel multi-authority attributed-based identification method, which realizes distributed attribute key generation and simplifies the pairwise authentication process of multi-authority. It can not only address the key escrow problem of one single authority, but also reduce the problem of high communication overhead and heavy burden of multi-authority. The analyzed results show that the proposed scheme has better comprehensive performance in trusted data storage and access control for power cyber-physical system.

The Internet of Things (IoT) provides significant benefits for industry due to connect the devices together through the internet. Attribute-Based Encryption (ABE) is a technique can enforce an access control over data to guarantee the data security. In this paper, we propose an ABE scheme for data in industrial IoT. The scheme achieves both security and high performance. When there is a shared subpolicy among the access policies of a sensor, the scheme optimizes the encryption of the messages. Through analysis and simulation, we show that our solution is security and efficient.

This paper analyzes the necessity of artificial intelligence and big data in the security application of regulatory places. The author studies the specific application of artificial intelligence and big data in ideological dynamics management, access control system, video surveillance system, emergency alarm system, perimeter control system, police inspection system, daily behavior management, and system implementation management. The author puts forward how to do technical integration, improve information sharing, strengthen the construction of talents, and increase management fund expenditure. The purpose of this paper is to enhance the security management level of regulatory places and optimize the management environment of regulatory places.

The data transmitted via the network may be vulnerable to cyber attacks in networked inverted pendulum system (NIPS), how to detect cyber attacks is a challenging issue. To solve this problem, this paper investigates a honeypot-based attack detection method for NIPS. Firstly, honeypot for NIPS attack detection (namely NipsPot) is constructed by deceptive environment module of a virtual closed-loop control system, and the stealthiness of typical covert attacks is analysed. Secondly, attack data is collected by NipsPot, which is used to train supported vector machine (SVM) model for attack detection. Finally, simulation results demonstrate that NipsPot-based attack detector can achieve the accuracy rate of 99.78%, the precision rate of 98.75%, and the recall rate of 100%.

The cipher-text homomorphism encryption algorithm (homomorphic encryption) are used for the cloud safe and to solve the integrity, availability and controllability of information. For homomorphic encryption, we, by Topsnut-gpw technique, design: degree-sequence homomorphisms and their inverses, degree-sequence homomorphic chain, graph-set homomorphism, colored degree-sequence matrices and every-zero Cds-matrix groups, degree-coinciding degree-sequence lattice, degree-joining degree-sequence lattice, as well as degree-sequence lattice homomorphism, since number-based strings made by Topsnut-gpws of topological coding are irreversible, and Topsnut-gpws can realize: one public-key corresponds two or more privatekeys, and more public-key correspond one or more private-keys for asymmetric encryption algorithm.

The results of evaluating explanations of the black-box model for prediction are presented. The XAI evaluation is realized through the different principles and characteristics between black-box model explanations and XAI labels. In the field of high-dimensional prediction, the black-box model represented by neural network and ensemble models can predict complex data sets more accurately than traditional linear regression and white-box models such as the decision tree model. However, an unexplainable characteristic not only hinders developers from debugging but also causes users mistrust. In the XAI field dedicated to ``opening'' the black box model, effective evaluation methods are still being developed. Within the established XAI evaluation framework (MDMC) in this paper, explanation methods for the prediction can be effectively tested, and the identified explanation method with relatively higher quality can improve the accuracy, transparency, and reliability of prediction.

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.

Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community. However, most of the prior arts along this line (a) merely adopt local information to perform modality alignment and (b) ignore the diversity of learning difficulty across different subjects. In this paper, we propose a novel framework to jointly address the above-mentioned issues. Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered. Compared with the existing methods, we introduce a global loss into the modality alignment process. The global component of the loss is driven by the identity classification. Theoretically, we show that minimizing the loss could maximize the distance between embeddings across different identities while minimizing the distance between embeddings belonging to the same identity, in a global sense (instead of a mini-batch). Targeting at (b), we propose a dynamic reweighting scheme to better explore the hard but valuable identities while filtering out the unlearnable identities. Experiments show that the proposed method outperforms the previous methods in multiple settings, including voice-face matching, verification and retrieval.

This paper is concerned with the problem of adaptive finite-time prescribed performance control for a category of uncertain nonlinear systems subject to fuzzy dead-zone input. Via combining the technologies of command filter and backstepping control, the ``singularity'' and the ``explosion of complexity'' issues within controller design procedure are avoided. Moreover, by designing a state observer and utilizing the center-of-gravity theorem, the unmeasured states of system are estimated and the fuzzy issue result from fuzzy dead-zone input is disposed, respectively. Meanwhile, a finite-time fuzzy controller is constructed via combining with finite-time stability criterion, which guarantees all the signals in closed-loop system are convergent and the trajectory of tracking error also strictly evolves within a predefined range in finite time. At last, some simulation results confirm the viability of presented theoretical results.

Electromagnetic (EM) radiation is an inherent phenomenon in the operation of electronic information equipment. The side-channel attack, malicious hardware and software implantation attack by using the EM radiation are implemented to steal information. This form of attacks can be used in air-gap information equipment, which bring great danger for information security. The malicious implantation hidden in circuits are difficult to detect. How to detect the implantation is a challenging problem. In this paper, a malicious hardware implantation is analyzed. A method that leverages EM signals for Trojan-embedded computer video cable detection is proposed. The method neither needs activating the Trojan nor requires near-field probe approaching at close. It utilizes recognizable patterns in the spectrum of EM to predict potential risks. This paper focuses on the extraction of feature vectors via the empirical mode decomposition (EMD) algorithm. Intrinsic mode functions (IMFs) are analyzed and selected to be eigenvectors. Using a common classification technique, we can achieve both effective and reliable detection results.

This paper presents a method optimizing the thrust force of a Micro Electro Mechanical System (MEMS) Permanent Magnet Linear Motor, based on harmonic current injection. Fourier decomposition is implemented to the air gap flux density of the motor to derive the fitting expression of the thrust force dependent to exciting current. Through analyzing the thrust force ripple of sinusoidal current excitement, the paper comes up with the strategy of harmonic current injection to eliminate the ripple component in the thrust force waveform. Mathematical demonstration is given that injecting harmonic current can totally eliminate the ripple caused by odd component of vertical air gap magnetic induction intensity. Simulation verification is implemented based on the 3rd and 7th harmonic injection control strategy, proving that the method is feasible for the thrust ripple is reduced to 4.3% of the value before optimazation. Experimental results lead to the consistent conclusion that the strategy shows good steady-state and dynamic performance.

In case of wildfire, particles generated in combustion are in complex law of motion under the influence of flame temperature, airflow and lots of electrons and ions. They would distort the space electric field, and lead to gap discharge. This paper develops a multi-physics coupling calculation model of fluid, temperature, electric field and particle movement by combining the rod-plate gap experiment that simulates the wildfire condition. It analyzes the motion state of ash particles in flames, studies the charged particles of different polarity separately, and explores the impact of particle properties on the electric field of gap space by combining the distribution of particles. Results have shown that there are differences in the motion state of charged particles of different polarity, and the electrode will absorb some particles with different charges, while charged particles with the same polarity as the electrode will move away from the electrode in random motion. Particles of different properties (particle size, relative dielectric constant) have different impacts on the electric field of gap space, but they all promote the discharge propagation.

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations hold the same set of samples but with disjoint features and only one organization owns the labels, has received increased attention. This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. The attack methods consider the most stringent setting that the adversary controls only the trained vertical FL model and the model predictions, relying on no background information of the attack target's data distribution. We first propose two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output. We further design a general attack method based on multiple prediction outputs accumulated by the adversary to handle complex models, such as neural networks (NN) and random forest (RF) models. Experimental evaluations demonstrate the effectiveness of the proposed attacks and highlight the need for designing private mechanisms to protect the prediction outputs in vertical FL.