Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

Singular spectrum analysis for the time-variable seasonal signals from GPS in Yunnan Province

Studying the seasonal deformation in GPS time series is important to interpreting geophysical contributors and identifying unmodeled and mismodeled seasonal signals.Traditional seasonal signal extraction used the least squares method,which models seasonal deformation as a constant seasonal amplitude and phase.However,the seasonal variations are not constant from year to year,and the seasonal amplitude and phase are time-variable.In order to obtain the time-variable seasonal signal in the GPS station coordinate time series,singular spectrum analysis(SSA)is conducted in this study.We firstly applied the SSA on simulated seasonal signals with different frequencies 1.00 cycle per year(cpy),1.04 cpy and with time-variable amplitude are superimposed.It was found that SSA can successfully obtain the seasonal variations with different frequencies and with time-variable amplitude superimposed.Then,SSA is carried out on the GPS observations in Yunnan Province.The results show that the time-variable amplitude seasonal signals are ubiquitous in Yunnan Province,and the timevariable amplitude change in 2019 in the region is extracted,which is further explained by the soil moisture mass loading and atmospheric pressure loading.After removing the two loading effects,the SSA obtained modulated seasonal signals which contain the obvious seasonal variations at frequency of 1.046 cpy,it is close with the GPS draconitic year,1.040 cpy.Hence,the time-variable amplitude changes in 2019 and the seasonal GPS draconitic year in the region could be discriminated successfully by SSA in Yunnan Province.

A reweighted damped singular spectrum analysis method for robust seismic noise suppression

(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

Performance analysis model for real-time Ethernet-based computer numerical control system

In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.

Real-Time Multimodal Biometric Authentication of Human Using Face Feature Analysis

As multimedia data sharing increases,data security in mobile devices and its mechanism can be seen as critical.Biometrics combines the physiological and behavioral qualities of an individual to validate their character in real-time.Humans incorporate physiological attributes like a fingerprint,face,iris,palm print,finger knuckle print,Deoxyribonucleic Acid(DNA),and behavioral qualities like walk,voice,mark,or keystroke.The main goal of this paper is to design a robust framework for automatic face recognition.Scale Invariant Feature Transform(SIFT)and Speeded-up Robust Features(SURF)are employed for face recognition.Also,we propose a modified Gabor Wavelet Transform for SIFT/SURF(GWT-SIFT/GWT-SURF)to increase the recognition accuracy of human faces.The proposed scheme is composed of three steps.First,the entropy of the image is removed using Discrete Wavelet Transform(DWT).Second,the computational complexity of the SIFT/SURF is reduced.Third,the accuracy is increased for authentication by the proposed GWT-SIFT/GWT-SURF algorithm.A comparative analysis of the proposed scheme is done on real-time Olivetti Research Laboratory(ORL)and Poznan University of Technology(PUT)databases.When compared to the traditional SIFT/SURF methods,we verify that the GWT-SIFT achieves the better accuracy of 99.32%and the better approach is the GWT-SURF as the run time of the GWT-SURF for 100 images is 3.4 seconds when compared to the GWT-SIFT which has a run time of 4.9 seconds for 100 images.

A Mathematical Model of Real-Time Simulation and the Convergence Analysis on Real-Time Runge-Kutta Algorithms

In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation and the convergent order of real-time algorithm is proved.

Integrated Real-Time Big Data Stream Sentiment Analysis Service

Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

Real-Time Computer Analytical Application in Pressure Transient Analysis of Petroleum Reservoirs

Today with certainty, the petroleum industry is fostering sanguinely the fields’ development programs for the optimization of reservoir characterization through worth-full appliances of computer analysis techniques. The time element is of prime importance for optimistic petroleum development projects. Therefore, the frontline of “Real-time Analysis” is added into the applications of computer solving techniques for achieving and sketching up the real-time cost effectiveness in analyzing field development programs. It focuses on the phases of real-time well test data acquisition system, real-time secure access to the well test data either on field or in office and real-time data interpretation unit. This interface will yield the productive results for the field of reservoir’s pressure transient analysis and wells’ systems analysis by following the up-to-date preferred, accurate and effective well test analytical principles with modern real-time computer applications and techniques. It also lays emphasis for the comfort and reliability of data in creating best interpersonal working modes within a reputable and esteemed petroleum development organization.

Real-Time Grouting Monitoring and Visualization Analysis System for Dam Foundation Curtain Grouting

A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model(GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.

FLUORESCENCE SPECTRUM ANALYSIS OF ETHER-WATER SOLUTION BASED ON GAUSSIAN DECOMPOSITION METHOD

The fluorescence spectrum of the ether-water solution excited by the ultraviolet light with the wavelength of 245 nm is experimentally detected. Based on the second derivative analysis, the fluorescence spectrum of the ether-water solution is used as Gaussian decomposition and seven Gaussian spectral lines are obtained. The center wavelength, the peak intensity and the half peak bandwidth of each Gaussian spectral line are measured, and the multi-peak fitting is made by using Gaussian primitive parameters. The highest and the lowest oscillation energy level differences in the ground state of each Gaussian spectrum are calculated. It is found that there are seven types of luminescent association molecules formed by ether and water molecules in different configurations existed in the solution. The location of each optimum absorption wavelength and the half peak bandwidth of the Gaussian spectral line is different. The energy level difference with the central wavelength of 304 nm attains the maximum value The result can contribute to the study of the molecular association in ether-water solution.

Multi-Strategy Dynamic Spectrum Access in Cognitive Radio Networks: Modeling, Analysis and Optimization

Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.

Short-range Climate Prediction Experiment of the Southern Oscillation Index Based on the Singular Spectrum Analysis

The Southern Oscillation Index (SOI) time series is analyzed by means of the singular spectrum analysis (SSA) method with 60-month window length. Two major oscillatory pairs are found in the series whose periods are quasi-four and quasi-two years respectively. The auto-regressive model, which is developed on the basis of the Maximum Entropy Spectrum Analysis, is fitted to each of the 9 leading components including the oscillatory pairs. The prediction of SOI with the 36-month lead is obtained from the reconstruction of these extrapolated series. Correlation coefficient between predicted series and 5 months running mean of observed series is up to 0.8. The model can successfully predict the peak and duration of the strong ENSO event from 1997 to 1998. It's also shown that the proper choice of reconstructed components is the key to improve the model prediction.

Anti-Noise Performance of the FT Continuous Zoom Analysis Method for Discrete Spectrum

As a discrete spectrum correction method, the Fourier transform （FT） continuous zoom analysis method is widely used in vibration signal analysis, but little effort had been made on this method＇s anti-noise performance. It is widely believed that the analysis accuracy of the method can be substantially improved by increasing the zoom multiple, however, with the zoom multiple increases, the frequency estimation accuracy may decline sometimes in practices. Aiming at the problems above, this paper analyzes the sources of frequency estimation error when a harmonic signal mixed with and without noise is processed using the FT continuous zoom analysis. According to the characteristics that the local maximum of the zoom spectrum may be wrongly selected when the signal is corrupted with noise, the number of wrongly selected spectrum lines is deduced under different signal-to-noise ratio and local zoom multiple, and then the maximum frequency estimation error is given accordingly. The validity of the presented analysis is confirmed by simulations results. The frequency estimation accuracy of this method will not improve any more under the influence of noise, and there is a best zoom multiple, when the zoom multiple is larger than the best zoom multiple; the maximum frequency estimation error will fluctuate back and forth. The best zoom multiple curves under different signal-to-noise ratios given provide a theoretical basis for the choice of the appropriate zoom multiples of the FT continuous zoom analysis method in engineering applications.

Frequency spectrum analysis on micro-seismic signal of rock bursts induced by dynamic disturbance

Blasting and breaking of hard roof are main inducing causes of rock bursts in coal mines with danger of rock burst,and it is important to find out the frequency spectrum distribution laws of these dynamic stress waves and rock burst waves for researching the mechanism of rock burst.In this paper,Fourier transform as a micro-seismic signal conversion method of amplitude-time character to amplitude-frequency character is used to analyze the frequency spectrum characters of micro-seismic signal of blasting,hard roof breaking and rock bursts induced by the dynamic disturbance in order to find out the difference and relativity of different signals.The results indicate that blasting and breaking of hard roof are high frequency signals,and the peak values of dominant frequency of the signals are single.However,the results indicate that the rock bursts induced by the dynamic disturbance are low frequency signals,and there are two obvious peak values in the amplitude-frequency curve witch shows that the signals of rock bursts are superposition of low frequency signals and high frequency signals.The research conclusions prove that dynamic disturbance is necessary condition for rock bursts,and the conclusions provide a new way to research the mechanism of rock bursts.

Geometric Covariance Modeling for Surface Variation of Compliant Parts Based on Hybrid Polynomial Approximation and Spectrum Analysis

Part variation characterization is essential to analyze the variation propagation in flexible assemblies. Aiming at two governing types of surface variation,warping and waviness,a comprehensive approach of geometric covariance modeling based on hybrid polynomial approximation and spectrum analysis is proposed,which can formulate the level and the correlation of surface variations accurately. Firstly,the form error data of compliant part is acquired by CMM. Thereafter,a Fourier-Legendre polynomial decomposition is conducted and the error data are approximated by a Legendre polynomial series. The weighting coefficient of each component is decided by least square method for extracting the warping from the surface variation. Consequently,a geometrical covariance expression for warping deformation is established. Secondly,a Fourier-sinusoidal decomposition is utilized to approximate the waviness from the residual error data. The spectrum is analyzed is to identify the frequency and the amplitude of error data. Thus,a geometrical covariance expression for the waviness is deduced. Thirdly,a comprehensive geometric covariance model for surface variation is developed by the combination the Legendre polynomials with the sinusoidal polynomials. Finally,a group of L-shape sheet metals is measured along a specific contour,and the covariance of the profile errors is modeled by the proposed method. Thereafter,the result is compared with the covariance from two other methods and the real data. The result shows that the proposed covariance model can match the real surface error effectively and represents a tighter approximation error compared with the referred methods.

SAR tomography imaging via higher-order spectrum analysis

To deal with the non-Caussian noise in standard 2-D SAR images, the deramped signal in imaging plane, and the possible symmetric distribution of complex noise, the fourth-order cumulant of complex process is introduced into SAR tomography. With the estimated AR parameters of ARMA model of noise through Yule-Walker equation, the signal series of height is pre-filtered. Then, through ESPRIT, the spectrum is obtained and the aperture in height direction is synthesized. Finally, the SAR tomography imaging of scene is achieved. The results of processing on signal with non-Gaussian noise demonstrate the robustness of the proposed method. The tomography imaging of the scenes shows that the higher-order spectrum analysis is feasible in the application.