Biblio

Security of physical layer key generation is based on the randomness and reciprocity of wireless fading channel, which has attracted more and more attention in recent years. This paper proposes a rate adaptive key agreement scheme and utilizes the received signal strength (RSS) of the channel between two wireless devices to generate the key. In conventional information reconciliation process, the bit inconsistency rate is usually eliminated by using the filter method, which increases the possibility of exposing the generated key bit string. Building on the strengths of existing secret key extraction approaches, this paper develops a scheme that uses Reed-Solomon (RS) codes, one of forward error correction channel codes, for information reconciliation. Owing to strong error correction performance of RS codes, the proposed scheme can solve the problem of inconsistent key bit string in the process of channel sensing. At the same time, the composition of RS codes can help the scheme realize rate adaptation well due to the construction principle of error correction code, which can freely control the code rate and achieve the reconciliation method of different key bit string length. Through experiments, we find that when the number of inconsistent key bits is not greater than the maximum error correction number of RS codes, it can well meet the purpose of reconciliation.

In this paper, the adaptive event-triggered asymptotic tracking control with guaranteed performance for a single link robot manipulator (SLRM) system driven by the brush DC motor is studied. Fuzzy logic systems (FLS) is used to approximate unknown nonlinear functions. By introducing a finite time performance function (FTPF), the tracking error of the system can converge to the compact set of the origin in finite time. In addition, by introducing the smooth function and some positive integral functions, combined with the boundary estimation method and adaptive backstepping technique, the asymptotic tracking control of the system is realized. Meanwhile, event-triggered mechanism is introduced to reduce the network resources of the system. Finally, a practical example is given to prove the effectiveness of the theoretical research.

Fog computing plays a critical role in the provisioning of computing tasks in the context of Internet of Things (IoT) services. However, the security of IoT services against breaches and attacks relies heavily on the security of fog resources, which must be properly implemented and managed. Increasing security investments and integrating the security aspect into the core processes and operations of fog computing including resource management will increase IoT service protection as well as the trustworthiness of fog service providers. However, this requires careful modeling of the security requirements of IoT services as well as theoretical and experimental evaluation of the tradeoff between security and performance in fog infrastructures. To this end, this paper explores a new model for fog resource allocation according to security and Quality of Service (QoS). The problem is modeled as a multi-objective linear optimization problem and solved using conventional, off-the-shelf optimizers by applying the preemptive method. Specifically, two objective functions were defined: one representing the satisfaction of the security design requirements of IoT services and another that models the communication delay among the different virtual machines belonging to the same service request, which might be deployed on different intermediary fog nodes. The simulation results show that the optimization is efficient and achieves the required level of scalability in fog computing. Moreover, a tradeoff needs to be pondered between the two criteria during the resource allocation process.

Increasing consumer experience and companies inner quality presents a direct demand of different requirements on supply chain traceability. Typically, existing solutions have separate data storages which eventually provide limited support when multiple individuals are included. Therefore, the block-chain-based methods are utilized to defeat these deficiencies by generating digital illustrations of real products to following several objects at the same time. Nevertheless, they actually cannot identify the change of products in manufacturing methods. The connection between components included in the production decreased, whereby the ability to follow a product’s origin reduced consequently. In this paper, a methodology is recommended which involves using a Block-chain in Supply Chain Traceability, to solve the issues of manipulations and changes in data and product source. The method aims to improve the product’s origin transparency. Block-chain technology produces a specific method of storing data into a ledger, which is raised on many end-devices such as servers or computers. Unlike centralized systems, the records of the present system are encrypted and make it difficult to be manipulated. Accordingly, this method manages the product’s traceability changes. The recommended system is performed for the cheese supply chain. The result were found to be significant in terms of increasing food security and distributors competition.

With the rapid improvement of China's industrial production level, there are an increasing number of industrial enterprises and kinds of products. The quality and safety supervision of industrial products is an important step to ensure people's livelihood safety. The current supervision includes a number of processes, such as risk monitoring, public opinion analysis, supervision, spot check and postprocessing. The lack of effective information integration and sharing between the above processes cannot support the implementation of whole-chain regulation well. This paper proposes a whole-chain supervision method of industrial product quality and safety based on a knowledge graph, which integrates massive and complex data of the whole chain and visually displays the relationships between entities in the regulatory process. This method can effectively solve the problem of information islands and track and locate the quality problems of large-scale industrial products.

With the continuous development of world economy, the trade and connection between countries are getting closer, in which ports are playing an increasingly important role. However, due to the inherent complexity of port operational environment, ports are exposed to various types of hazards and more likely to encounter risks with high frequency and serious consequences. Therefore, proper and effective risk management of ports is particularly essential and necessary. In this research, literature from three aspects including risk assessment of port operations and service, safety management of dangerous goods, and port supply chain risk management was collected and investigated, in order to put forward the future research direction related to the risk management of port operations. The research results show that, firstly, most of the current research mainly focuses on the operational risk of traditional ports and a lot of relevant achievements have been seen. However, few scholars have studied the risk issues of smart ports which are believed to be the trend of future with the rapid development and application of high and new technologies. Thus, it is suggested that more attention should be shifted to the identification and assessment of operational risks of smart ports considering their characteristics. Secondly, although the risk evaluation systems of port operational safety have been established and widely studied, more efforts are still needed in terms of the suitability and effectiveness of the proposed indicators, especially when dangerous goods are involved. Thirdly, risk management of port supply chain is another popular topic, in which, one of the main difficulties lies on the collection of risk related statistics data due to the fact that port supply chain systems are usually huge and complex. It is inevitably that the evaluation results will lack objectivity to some extent. Therefore, it calls for more research on the risk assessment of port supply chains in a quantitative manner. In addition, resilience, as an emerging concept in the transportation field, will provide a new angle on the risk management of port supply chains.

The modern electronics supply chain is a globalized marketplace with the increasing threat of counterfeit integrated circuits (ICs) being installed into mission critical systems. A number of methods for detecting counterfeit ICs exist; however, effective test and evaluation (T&E) methods to assess the confidence of detecting recycled ICs are needed. Additionally, methods for the trustworthy tracking of recycled ICs in the supply chain are also needed. In this work, we propose a novel methodology to address the detection and tracking of recycled ICs at each stage of the electronics supply chain. We present a case study demonstrating our assessment model to calculate the confidence levels of authentic and recycled ICs, and to confidently track these types of ICs throughout the electronics supply chain.

Under the background of "Internet +", in order to solve the problem of deeply mining credit risk behind online supply chain financial big data, this paper proposes an online supply chain financial credit risk assessment method based on deep belief network (DBN). First, a deep belief network evaluation model composed of Restricted Boltzmann Machine (RBM) and classifier SOFTMAX is established, and the performance evaluation test of three kinds of data sets is carried out by using this model. Using factor analysis to select 8 indicators from 21 indicators, and then input them into RBM for conversion to form a more scientific evaluation index, and finally input them into SOFTMAX for evaluation. This method of online supply chain financial credit risk assessment based on DBN is applied to an example for verification. The results show that the evaluation accuracy of this method is 96.04%, which has higher evaluation accuracy and better rationality compared with SVM method and Logistic method.

Based on the analysis of cluster supply chain risk characteristics, starting from the analysis of technical risk dimensions, information risk dimensions, human risk dimensions, and capital risk dimensions, a cluster supply chain risk severity assessment index system is designed. The fuzzy C-means clustering algorithm based on data flow is used to cluster each supply chain, analyze the risk severity of the supply chain, and evaluate the decision of the supply chain risk severity level based on the cluster weights and cluster center range. Based on the analytic hierarchy process, the risk severity of the entire clustered supply chain is made an early warning decision, and the clustered supply chain risk severity early warning level is obtained. The results of simulation experiments verify the feasibility of the decision method for cluster supply chain risk severity, and improve the theoretical support for cluster supply chain risk severity prediction.

This paper establishes a novel approach to supply chain risk management (SCRM), through establishing a risk assessment framework addressing the importance of SCRM and supply chain visibility (SCV). Through a quantitative assessment and empirical evidence, the paper also discusses the specific risks within the manufacturing industry. Based on survey data collected and a case study from Asia, the paper finds that supplier delays and poor product quality can be considered as prevailing risks relevant to the manufacturing industry. However, as supply chain risks are inter-related, one must increase supply chain visibility to fully consider risk causes that ultimately lead to the risk effects. The framework established can be applied to different industries with the view to inform organisations on prevailing risks and prompt motivate improvement in supply chain visibility, thereby, modify risk management strategies. Through suggesting possible risk sources, organisations can adopt proactive risk mitigation strategies so as to more efficiently manage their exposure.

Information security of logistics services. Information security of logistics services is understood as a complex activity aimed at using information and means of its processing in order to increase the level of protection and normal functioning of the object's information environment. At the same time the main recommendations for ensuring information security of logistics processes include: logistics support of processes for ensuring the security of information flows of the enterprise; assessment of the quality and reliability of elements, reliability and efficiency of obtaining information about the state of logistics processes. However, it is possible to assess the level of information security within the organization's controlled part of the supply chain through levels and indicators. In this case, there are four levels and elements of information security of supply chains.

Previous research on frequency hopping (FH) signal recognition utilizing deep learning only focuses on single-label signal, but can not deal with overlapped FH signal which has multi-labels. To solve this problem, we propose a new FH signal recognition method based on fully convolutional networks (FCN). Firstly, we perform the short-time Fourier transform (STFT) on the collected FH signal to obtain a two-dimensional time-frequency pattern with time, frequency, and intensity information. Then, the pattern will be put into an improved FCN model, named FH-FCN, to make a pixel-level prediction. Finally, through the statistics of the output pixels, we can get the final classification results. We also design an algorithm that can automatically generate dataset for model training. The experimental results show that, for an overlapped FH signal, which contains up to four different types of signals, our method can recognize them correctly. In addition, the separation of multiple FH signals can be achieved by a slight improvement of our method.

Specific emitter identification (SEI) is a physical-layer-based approach for enhancing wireless communication network security. A well-done SEI method can be widely applied in identifying the individual wireless communication device. In this paper, we propose a novel specific emitter identification method based on variational mode decomposition and histogram of oriented gradient (VMD-HOG). The signal is decomposed into specific temporal modes via VMD and HOG features are obtained from the time-frequency spectrum of temporal modes. The performance of the proposed method is evaluated both in single hop and relaying scenarios and under three channels with the number of emitters varying. Results depict that our proposed method provides great identification performance for both simulated signals and realistic data of Zigbee devices and outperforms the two existing methods in identification accuracy and computational complexity.

This work describes a concept for extending the Network Time Security (NTS) protocol to enable implementation- independent communication between the NTS key establishment (NTS-KE) server and the connected time server(s). It Alls a specification gap left by RFC 8915 for securing the Network Time Protocol (NTP) and enables the centralized and public deployment of an NTS key management server that can support both secured NTP and secured PTP.

{With the rapid development of cyberspace, cyber security incidents are increasing, and the means and types of network attacks are becoming more and more complex and refined, which brings greater challenges to security risk control. First, the knowledge graph technology is used to construct a cyber security knowledge graph based on ontology to realize multi-source heterogeneous security big data fusion calculation, and accurately express the complex correlation between different security entities. Furthermore, for cyber security risk control, a key node assessment method for security risk diffusion is proposed. From the perspectives of node communication correlation and topological level, the calculation method of node communication importance based on improved PageRank Algorithm and based on the improved K-shell Algorithm calculates the importance of node topology are studied, and then organically combine the two calculation methods to calculate the importance of different nodes in security risk defense. Experiments show that this method can evaluate the importance of nodes more accurately than the PageRank algorithm and the K-shell algorithm.

Magnetized Liner Inertial Fusion (MagLIF) is a magneto-inertial fusion concept that relies on fuel magnetization, laser preheat, and a magnetically driven implosion to produce fusion conditions. In MagLIF, the target is a roughly 10 mm long, 5 mm diameter, 0.5 mm thick, cylindrical beryllium shell containing 1 mg/cm 3 D 2 gas. An axial magnetic field on the order of 10 T is applied to the target, and several kJ of laser energy is deposited into the fuel. Up to 20 MA of current is driven axially through the beryllium target, causing it to implode over approximately 100 ns. The implosion produces a 100-μm diameter, 8-mm tall fuel column with a burn-averaged ion temperature of several keV, that generates 10 11 -10 13 DD neutrons.

Auto-magnetizing (AutoMag) liners 1 have demonstrated strong precompressed axial magnetic field production (\textbackslashtextgreater100 T) and remarkable cylindrical implosion uniformity during experiments 2 on the Z accelerator. However, both axial field production and implosion uniformity require further optimization to support use of AutoMag targets in magnetized liner inertial fusion (MagLIF) experiments. Recent experimental study on the Mykonos accelerator has provided data on the initiation and evolution of dielectric flashover in AutoMag targets; these results have directly enabled advancement of magnetohydrodynamic (MHD) modeling protocols used to simulate AutoMag liner implosions. Using these modeling protocols, we executed three-dimensional MHD simulations focused on improving AutoMag target designs, specifically seeking to optimize axial magnetic field production and enhance cylindrical implosion uniformity for MagLIF. By eliminating the previously used driver current prepulse and reducing the helical gap widths in AutoMag liners, simulations indicate that the optimal 30-50 T range of precompressed axial magnetic field for MagLIF can be accomplished concurrently with improved cylindrical implosion uniformity, thereby enabling an optimally premagnetized magneto-inertial fusion implosion with high cylindrical uniformity.

X-ray radiography has been used to diagnose a wide variety of experiments at the Z facility including inertial confinement fusion capsule implosions, the growth of the magneto-Rayleigh-Taylor instability in solid liners, and the development of helical structures in axially magnetized liner implosions. In these experiments, the Z Beamlet laser (1 kJ, 1 ns) was used to generate the x-ray source. An alternate x-ray source is desirable in experiments where the Z Beamlet laser is used for another purpose (e.g., preheating the fuel in magnetized liner inertial fusion experiments) or when multiple radiographic lines of sight are necessary.

Magnetized Liner Inertial Fusion (MagLIF) is a magneto-inertial fusion (MIF) concept being studied on the Z-machine at Sandia National Laboratories. MagLIF relies on quasi-adiabatic heating of a gaseous deuterium (DD) fuel and flux compression of a background axially oriented magnetic field to achieve fusion relevant plasma conditions. The magnetic flux per fuel radial extent determines the confinement of charged fusion products and is thus of fundamental interest in understanding MagLIF performance. It was recently shown that secondary DT neutron spectra and yields are sensitive to the magnetic field conditions within the fuel, and thus provide a means by which to characterize the magnetic confinement properties of the fuel. 1 , 2 , 3 We utilize an artificial neural network to surrogate the physics model of Refs. [1] , [2] , enabling Bayesian inference of the magnetic confinement parameter for a series of MagLIF experiments that systematically vary the laser preheat energy deposited in the target. This constitutes the first ever systematic experimental study of the magnetic confinement properties as a function of fundamental inputs on any neutron-producing MIF platform. We demonstrate that the fuel magnetization decreases with deposited preheat energy in a fashion consistent with Nernst advection of the magnetic field out of the hot fuel and diffusion into the target liner.

Graphs are the dominant formalism for modeling multi-agent systems. The algebraic connectivity of a graph is particularly important because it provides the convergence rates of consensus algorithms that underlie many multi-agent control and optimization techniques. However, sharing the value of algebraic connectivity can inadvertently reveal sensitive information about the topology of a graph, such as connections in social networks. Therefore, in this work we present a method to release a graph’s algebraic connectivity under a graph-theoretic form of differential privacy, called edge differential privacy. Edge differential privacy obfuscates differences among graphs’ edge sets and thus conceals the absence or presence of sensitive connections therein. We provide privacy with bounded Laplace noise, which improves accuracy relative to conventional unbounded noise. The private algebraic connectivity values are analytically shown to provide accurate estimates of consensus convergence rates, as well as accurate bounds on the diameter of a graph and the mean distance between its nodes. Simulation results confirm the utility of private algebraic connectivity in these contexts.

Ubiquitous Electric Internet of Things (UEIoT) is the next generation electrical energy networks. The distributed and open structure of UEIoT is weak and vulnerable to security threats. To solve the security problem of UEIoT terminal, in this paper, the interaction between smart terminals and the malicious attackers in UEIoT as a differential game is investigated. A complex decision-making process and interactions between the smart terminal and attackers are analyzed. Through derivation and analysis of the model, an algorithm for the optimal defense strategy of UEIoT is designed. The results lay a theoretical foundation, which can support UEIoT make a dynamic strategy to improve the defensive ability.

Reconfigurability is very popular in advanced highly integrated wireless communication circuits and systems, which is valuable for mitigating spectrum congestion and reducing signal interference. To reduce interference and meet the different wireless standards in different countries, frequency reconfigurable filters are promising. Concurrently, due to the conductor and semiconductor properties of VO2 at different temperatures or pressures, the phase transition characteristics of new material VO2 are applied to reconfigurable filters. This paper mainly discusses the application of phase transition characteristics of VO2 materials in filter design and proposes a frequency reconfigurable microstrip bandpass filter based on VO2 materials, in which the microstrip filter adopts the design form of end coupling. Through theoretical calculation, data analysis, and the establishment of the equivalent model of VO2 phase transition, a related design is proposed. An end-coupled microband bandpass filter centered at a reconfigurable frequency (6 GHz to 6.5 GHz) with fractional bandwidth of 2.8% has been designed, which shows consistent match with the expected ones and verify the validity of the proposed method.

System recommendation is currently on the rise: more and more e-commerce rely on this feature to give more privilege to their users. However, system recommendation still faces a lot of problems that can lead to its downfall. For instance, the cold start problem and lack of privacy for user’s data in system recommendation will make the quality of this system lesser than ever. Moreover, e-commerce also faces another significant issue which is the lack of social presence. Compared to offline shopping, online shopping in e-commerce may be seen as lacking human presence and sociability as it is more impersonal, cold, automated, and generally devoid of face-to-face interactions. Hence, all of those issues mentioned above may lead to the regression of user’s trust toward e-commerce itself. This study will focus on solving those problems using conversational design interaction in the form of a Virtual Agent. This Virtual Agent can help e-commerce gather user preferences and give clear and direct information regarding the use of user’s data as well as help the user find products, promo, or similar products that they seek in e-commerce. The final result of this solution is a high fidelity prototype designed using User-Centered Design Methodology and Natural Conversational Framework. The implementation of this solution is carried out in Shopee e-commerce by modifying their product recommendation system. This prototype was measured using the usability testing method for usability goals efficient to use and user experience goals helpful.

The secure functioning of automotive systems is vital to the safety of their passengers and other roadway users. One of the critical functions for safety is the controller area network (CAN), which interconnects the safety-critical electronic control units (ECUs) in the majority of ground vehicles. Unfortunately CAN is known to be vulnerable to several attacks. One such attack is the bus-off attack, which can be used to cause a victim ECU to disconnect itself from the CAN bus and, subsequently, for an attacker to masquerade as that ECU. A limitation of the bus-off attack is that it requires the attacker to achieve tight synchronization between the transmission of the victim and the attacker's injected message. In this paper, we introduce a schedule-based attack framework for the CAN bus-off attack that uses the real-time schedule of the CAN bus to predict more attack opportunities than previously known. We describe a ranking method for an attacker to select and optimize its attack injections with respect to criteria such as attack success rate, bus perturbation, or attack latency. The results show that vulnerabilities of the CAN bus can be enhanced by schedule-based attacks.

The Controller Area Network (CAN) bus is the de-facto standard for connecting the Electronic Control Units (ECUs) in automobiles. However, there are serious cyber-security risks due to the lack of security mechanisms. In order to mine the vulnerabilities in CAN bus, this paper proposes CAN-FT, a fuzz testing method for automotive CAN bus, which uses a Generative Adversarial Network (GAN) based fuzzy message generation algorithm and the Adaptive Boosting (AdaBoost) based anomaly detection mechanism to capture the abnormal states of CAN bus. Experimental results on a real-world vehicle show that CAN-FT can find vulnerabilities more efficiently and comprehensively.