Classification Method for Network Security Data Based on Multi-featured Extraction

2021 ◽  
Vol 30 (01) ◽  
pp. 2140006
Author(s):  
Yunchuan Kang ◽  
Jing Zhong ◽  
Ruofeng Li ◽  
Yuqiao Liang ◽  
Nian Zhang

A method of classifying network security data based on multi-featured extraction is proposed to address instability of a nonlinear time series in a network security threat. Cybersecurity information is divided in line with the principle of acquiring multiple attributes. On this basis, an adaptive adaptation estimation technology is optimized in analogue. With the proposed method, a cybersecurity information classification system is constructed according to the phase interval reconstruction principle so that a dynamic and autonomous adaptation estimation of the cybersecurity threat can be completed to ensure the feasibility of cybersecurity information classification. The experimental result proves that the cybersecurity information classification technology based on multi-attribute extraction can effectively guide chaos into adjacent orbits and reasonably control the training scale. Moreover, the accuracy of the estimation is guaranteed and the cybersecurity threat is estimated because of its high-speed convergence and strong proximity. Therefore, the proposed classification technology can assist professionals and backstage managers in guaranteeing security by facilitating receipt of information in a timely manner.

Author(s):  
Indah Yulia Prafitaning Tiyas ◽  
Ali Ridho Barakbah ◽  
Tri Harsono ◽  
Amang Sudarsono

Today, information technology is growing rapidly,all information can be obtainedmuch easier. It raises some new problems; one of them is unauthorized access to the system. We need a reliable network security system that is resistant to a variety of attacks against the system. Therefore, Intrusion Detection System (IDS) required to overcome the problems of intrusions. Many researches have been done on intrusion detection using classification methods. Classification methodshave high precision, but it takes efforts to determine an appropriate classification model to the classification problem. In this paper, we propose a new reinforced approach to detect intrusion with On-line Clustering using Reinforcement Learning. Reinforcement Learning is a new paradigm in machine learning which involves interaction with the environment.It works with reward and punishment mechanism to achieve solution. We apply the Reinforcement Learning to the intrusion detection problem with considering competitive learning using Pursuit Reinforcement Competitive Learning (PRCL). Based on the experimental result, PRCL can detect intrusions in real time with high accuracy (99.816% for DoS, 95.015% for Probe, 94.731% for R2L and 99.373% for U2R) and high speed (44 ms).The proposed approach can help network administrators to detect intrusion, so the computer network security systembecome reliable.Keywords: Intrusion Detection System, On-Line Clustering, Reinforcement Learning, Unsupervised Learning.


2009 ◽  
Vol 27 (1) ◽  
pp. 1-30 ◽  
Author(s):  
P. Prikryl ◽  
V. Rušin ◽  
M. Rybanský

Abstract. A sun-weather correlation, namely the link between solar magnetic sector boundary passage (SBP) by the Earth and upper-level tropospheric vorticity area index (VAI), that was found by Wilcox et al. (1974) and shown to be statistically significant by Hines and Halevy (1977) is revisited. A minimum in the VAI one day after SBP followed by an increase a few days later was observed. Using the ECMWF ERA-40 re-analysis dataset for the original period from 1963 to 1973 and extending it to 2002, we have verified what has become known as the "Wilcox effect" for the Northern as well as the Southern Hemisphere winters. The effect persists through years of high and low volcanic aerosol loading except for the Northern Hemisphere at 500 mb, when the VAI minimum is weak during the low aerosol years after 1973, particularly for sector boundaries associated with south-to-north reversals of the interplanetary magnetic field (IMF) BZ component. The "disappearance" of the Wilcox effect was found previously by Tinsley et al. (1994) who suggested that enhanced stratospheric volcanic aerosols and changes in air-earth current density are necessary conditions for the effect. The present results indicate that the Wilcox effect does not require high aerosol loading to be detected. The results are corroborated by a correlation with coronal holes where the fast solar wind originates. Ground-based measurements of the green coronal emission line (Fe XIV, 530.3 nm) are used in the superposed epoch analysis keyed by the times of sector boundary passage to show a one-to-one correspondence between the mean VAI variations and coronal holes. The VAI is modulated by high-speed solar wind streams with a delay of 1–2 days. The Fourier spectra of VAI time series show peaks at periods similar to those found in the solar corona and solar wind time series. In the modulation of VAI by solar wind the IMF BZ seems to control the phase of the Wilcox effect and the depth of the VAI minimum. The mean VAI response to SBP associated with the north-to-south reversal of BZ is leading by up to 2 days the mean VAI response to SBP associated with the south-to-north reversal of BZ. For the latter, less geoeffective events, the VAI minimum deepens (with the above exception of the Northern Hemisphere low-aerosol 500-mb VAI) and the VAI maximum is delayed. The phase shift between the mean VAI responses obtained for these two subsets of SBP events may explain the reduced amplitude of the overall Wilcox effect. In a companion paper, Prikryl et al. (2009) propose a new mechanism to explain the Wilcox effect, namely that solar-wind-generated auroral atmospheric gravity waves (AGWs) influence the growth of extratropical cyclones. It is also observed that severe extratropical storms, explosive cyclogenesis and significant sea level pressure deepenings of extratropical storms tend to occur within a few days of the arrival of high-speed solar wind. These observations are discussed in the context of the proposed AGW mechanism as well as the previously suggested atmospheric electrical current (AEC) model (Tinsley et al., 1994), which requires the presence of stratospheric aerosols for a significant (Wilcox) effect.


2021 ◽  
Vol 13 (8) ◽  
pp. 4425
Author(s):  
Taewoo Kim

In this paper, I investigate the relationship between previous going-concern audit opinions and subsequent asymmetric timeliness in accounting. Using the time-series and price-based models and conservatism proxy, I find that firms with going-concern audit opinions subsequently report losses in a more timely manner than firms that did not receive going-concern audit opinions. Furthermore, I also find that firms exiting going-concern audit opinions are more likely to report losses rather than gains in a timely manner, compared to firms non-exiting from going-concern opinions. This study extends the prior research by exploring the association between going-concern opinions and accounting conservatism from the perspective of client firms—that is, how firms behave strategically and conservatively to bypass going-concern opinions, once the firms had received previous going-concern opinions.


Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.


2014 ◽  
Vol 971-973 ◽  
pp. 1684-1687
Author(s):  
Xiu Juan Sun

this article from the various security threats facing the computer network, systematically introduces the network security technology. And in view of the campus network security issues, firstly analyzes the hidden dangers to the safety of network system in colleges and universities, and then from the build two aspects of security defense system and strengthen the safety management design of the campus network security policy. This paper study, the first thing I learned the main threat to the network security problem, and use the knowledge of security network security problems are analyzed. Secondly, based on the research of the network technology, campus network will also be faced with the security threat. Finally, the idea of established with P2DR model to establish campus network security defense system. And it is concluded that the building of a set of effective network security defense system is the solution Campus network main threats and hidden trouble of necessary ways and measures.


2005 ◽  
Author(s):  
Balaji Gopalan ◽  
Edwin Malkiel ◽  
Jian Sheng ◽  
Joseph Katz

High-speed in-line digital holographic cinematography was used to investigate the diffusion of droplets in locally isotropic turbulence. Droplets of diesel fuel (0.3–0.9mm diameter, specific gravity of 0.85) were injected into a 37×37×37mm3 sample volume located in the center of a 160-liter tank. The turbulence was generated by 4 spinning grids, located symmetrically in the corners of the tank, and was characterized prior to the experiments. The sample volume was back illuminated with two perpendicular collimated beams of coherent laser light and time series of in-line holograms were recorded with two high-speed digital cameras at 500 frames/sec. Numerical reconstruction generated a time series of high-resolution images of the droplets throughout the sample volume. We developed an algorithm for automatically detecting the droplet trajectories from each view, for matching the two views to obtain the three-dimensional tracks, and for calculating the time history of velocity. We also measured the mean fluid motion using 2-D PIV. The data enabled us to calculate the Lagrangian velocity autocorrelation function.


Author(s):  
Yumei Liu ◽  
Ningguo Qiao ◽  
Congcong Zhao ◽  
Jiaojiao Zhuang ◽  
Guangdong Tian

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.


Author(s):  
Jesús Franco-Robles ◽  
Alejandro De Lucio-Rangel ◽  
Karla A. Camarillo-Gómez ◽  
Gerardo I. Pérez-Soto ◽  
Jesús Rivera-Guillén

In this paper, a neuronal system with the ability to generate motion profiles and profiles of the ZMP in a 6DoF bipedal robot in the sagittal plane, is presented. The input time series for LSM training are movement profiles of the oscillating foot trajectory obtained by forward kinematics performed by a previously trained ANN multilayer perceptron. The profiles of objective movement for training are acquired from the analysis of the human walk. Based on a previous simulation of the bipedal robot, a profile of the objective ZMP will be generated for the y–axis and another for the z–axis to know its behavior during the training walk. As an experimental result, the LSM generates new motion profiles and ZMP, given a different trajectory with which it was trained. With the LSM it will be possible to propose new trajectories of the oscillating foot, where it will be known if this trajectory will be stable, by the ZMP, and what movement profile for each articulation will be required to reach this trajectory.


2016 ◽  
Vol 78 (6-9) ◽  
Author(s):  
Mohd Shahfizal Ruslan ◽  
Kamal Othman ◽  
Jaharah A.Ghani ◽  
Mohd Shahir Kassim ◽  
Che Hassan Che Haron

Magnesium alloy is a material with a high strength to weight ratio and is suitable for various applications such as in automotive, aerospace, electronics, industrial, biomedical and sports. Most end products require a mirror-like finish, therefore, this paper will present how a mirror-like finishing can be achieved using a high speed face milling that is equivalent to the manual polishing process. The high speed cutting regime for magnesium alloy was studied at the range of 900-1400 m/min, and the feed rate for finishing at 0.03-0.09 mm/tooth. The surface roughness found for this range of cutting parameters were between 0.061-0.133 µm, which is less than the 0.5µm that can be obtained by manual polishing. Furthermore, from the S/N ratio plots, the optimum cutting condition for the surface roughness can be achieved at a cutting speed of 1100 m/min, feed rate 0.03 mm/tooth, axial depth of cut of 0.20 mm and radial depth of cut of 10 mm. From the experimental result the lowest surface roughness of 0.061µm was obtained at 900 m/min with the same conditions for other cutting parameters. This study revealed that by milling AZ91D at a high speed cutting, it is possible to eliminate the polishing process to achieve a mirror-like finishing.


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