The analysis of application of data mining technology in the system of intrusion detection

With the economic development and the popularity of application of electronic computer, electronic commerce has rapid development. More and more commerce and key business has been carried on the lnternet because Internet has the features of interaction, openness, sharing and so on. However, during the daily commerce, people worry about the security of the network system. So a new technology which can detect the unusual behavior in time has been invented in order to protect the security of network system. The system of intrusion detection needs a lot of new technology to protect the data of the network system. The application of data mining technology in the system of intrusion detection can provide a better assistant to the users to analyze the data and improve the accuracy of the checking system.

Reliability Theory Study of Combined Mining Technology System in Surface Mines

The connection between all the single systems iu combined technology is analysed, and the mine engineering reliability is calculated in any area of the mines using combined technololgy system. The reliability design and distribution method in this system is put forward, thus the basis is set for the decision to raise the total production capability or the surface mines in which the combined system is used.

Research on the Data Mining Technology in College Students'Attendance System Based on the Big Data Architecture

With the development and improvement of the information technologies,the increasing of the upper application systems and the rapid expansion of the data accumulated in the campus information environment,a typical campus big data environment has initially been formed.Because of the characteristics of the higher education,students'mobility is great and their learning environment is uncertain,so that the students'attendance mostly used the manual naming.The student attendance system based on the big data architecture is relying on the campus network,and adopting the appropriate sensors.Through the data mining technology,combined with the campus One Card solution,we can realize the management of the attendance without naming in class.It can not only strengthen the management of the students,but can also improve the management levels of the colleges and universities.

Joint Bearing Mechanism of Coal Pillar and Backfilling Body in Roadway Backfilling Mining Technology

In the traditional mining technology,the coal resources trapped beneath surface buildings,railways,and water bodies cannot be mined massively,thereby causing the lower coal recovery and dynamic disasters.In order to solve the aforementioned problems,the roadway backfilling mining technology is developed and the joint bearing mechanism of coal pillar and backfilling body is presented in this paper.The mechanical model of bearing system of coal pillar and backfilling body is established,by analyzing the basic characteristics of overlying strata deformation in roadway backfilling mining technology.According to the Ritz method in energy variation principle,the elastic solution expression of coal pillar deformation is deduced in roadway backfilling mining technology.Based on elastic-viscoelastic correspondence principle,combining with the burgers rheological constitutive model and Laplace transform theory,the viscoelastic solution expression of coal pillar deformation is obtained in roadway backfilling mining technology.By analyzing the compressive mechanical property of backfilling body,the time formula required for coal pillar and backfilling body to play the joint bearing function in roadway backfilling mining technology is obtained.The example analysis indicates that the time is 140 days.The results can be treated as an important basis for theoretical research and process design in roadway backfilling mining technology.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

Analysis on The Application of Data Mining Technology in Computer Network Virus Defense

With the rapid development of computer network,the society has entered the information and digital era,it plays an important role in speeding up the pace of social development and providing more convenient services for people.However, the security problem of computer network is becoming more and more serious. All kinds of network viruses pose a great threat to the security of computer network.As the most advanced data processing technology currently, data mining technology can effectively resist the invasion of network virus to computer system,and plays an important role in improving the security of the computer network.This paper starts with the concept of data mining technology and the characteristics of computer network virus,and makes an in-depth analysis on the specific application of data mining technology in the computer network virus defense.

Development of Mining Technology and Equipment for Seafloor Massive Sulfide Deposits

Seafloor massive sulfide（SMS） deposits which consist of Au, Ag, Cu, and other metal elements, have been a target of commercial mining in recent decades. The demand for established and reliable commercial mining system for SMS deposits is increasing within the marine mining industry. The current status and progress of mining technology and equipment for SMS deposits are introduced. First, the mining technology and other recent developments of SMS deposits are comprehensively explained and analyzed. The seafloor production tools manufactured by Nautilus Minerals and similar mining tools from Japan for SMS deposits are compared and discussed in turn. Second, SMS deposit mining technology research being conducted in China is described, and a new SMS deposits mining tool is designed according to the environmental requirement. Finally, some new trends of mining technology of SMS deposits are summarized and analyzed. All of these conclusions and results have reference value and guiding significance for the research of SMS deposit mining in China.

Research on the big data feature mining technology based on the cloud computing

The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big data feature analysis and mining. The big data feature mining in the cloud computing environment is an effective method for the elficient application of the massive data in the information age. In the process of the big data mining, the method o f the big data feature mining based on the gradient sampling has the poor logicality. It only mines the big data features from a single-level perspective, which reduces the precision of the big data feature mining.

A Survey on Binary Code Vulnerability Mining Technology

With the increase of software complexity,the security threats faced by the software are also increasing day by day.So people pay more and more attention to the mining of software vulnerabilities.Although source code has rich semantics and strong comprehensibility,source code vulnerability mining has been widely used and has achieved significant development.However,due to the protection of commercial interests and intellectual property rights,it is difficult to obtain source code.Therefore,the research on the vulnerability mining technology of binary code has strong practical value.Based on the investigation of related technologies,this article firstly introduces the current typical binary vulnerability analysis framework,and then briefly introduces the research background and significance of the intermediate language;with the rise of artificial intelligence,a large number of machine learning methods have been tried to solve the problem of binary vulnerability mining.This article divides the current related binary vulnerabilities mining technology into traditional mining technology and machine learning mining technology,respectively introduces its basic principles,research status and existing problems,and briefly summarizes them.Finally,based on the existing research work,this article puts forward the prospect of the future research on the technology of binary program vulnerability mining.

Research on the Development of Ideological and Political Education Resources for College Students Based on the Data Mining Technology

With the rapid development of the computer technologies, the rapid spread and development of the network resources have a significant impact on people's thinking and lifestyle. The ideological and political education in colleges and universities should keep pace with the times, and examine the new ways and resources of the ideological and political education in colleges and universities from the perspective of the times. The high-tech has been applied to the development of the modem education, expanding the teaching space-time, improving the teaching methods of the ideological and political course, and enriching the teaching contents of the ideological and political theoretical courses. The development mechanism of the ideological and political education resources for college students based on the data mining technology is not only in line with the trend of the times, but can also play an ideal role in optimizing the ideological and political education mechanism for college students.

The application of data mining technology in data service of micro service architecture

This paper introduces the application of data mining technology in data service. Data mining is a new technology, but its application time is long, and the application effect is obvious. Data mining technology can be used in enterprise customer service system, which can help enterprises to find potential customers, while retaining the most valuable customers. We pointed out the necessity of establishing data mining technology and service intelligence analysis system based on the combination of intelligence analysis and service characteristics and processes, the method of data mining, knowledge management ideas applied to intelligence analysis and service system.

Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques

A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.

Ecological Risk Assessment of Shan Xin Mining Area Based on Remote Sensing and Geography Information System Technology

In this paper, introducing new remote sensing and geographic information technology to solve the problem of data collection and analysis, this makes the study of ecological risk assessment very quick and accurate. Taking the Shan Xin mining area of the tin mine in Lengshuijiang of Hunan Province as the research object, using the remote sensing image data of three periods in 2005, 2010 and 2015, the remote sensing image is classified carefully and the landscape classification map of the mining area is obtained. The ecological risk index is introduced and the ecological risk values are sampled and interpolated on the ArcGIS platform. The ecological risk spatial distribution map based on the landscape pattern index was obtained. The ecological risk was divided into 5 levels by using the Jenks natural classification method, and each ecological risk grade area was counted. The research results show that: from year 2005 to year 2010, landscape ecological risk trend of the mining area is growing up;the trend rising area of landscape ecological risk is mainly in the southwest and northeast of the Shan Xin mining field;the area of higher and high ecological risk is increasing year by year;and the trend of dispersed development in space is obvious;the development trend of ecological risk in the mining area is rapidly increasing;in 2010 - 2015, the higher and high ecological risk area decrease slightly with the increasing of area of grassland and residential low vulnerability of landscape types;the ecological risk area showed a slow decreasing trend. The research results provide an objective reference for decision making of ecological environment governance.

Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

KEY TECHNOLOGY OF SEMI-CONTINOUS CONVEYOR SYSTEM IN DEEP SURFACE MINE

The key technology of semi-continuous conveyor system decides the successful using of this system. The key technology includes optimal initial building depth of this system,the crusher moving step and the layout of conveyor system are studied, and their calculating models are built up.

“3-Section” Technology of Full-Dimension Mining in Flat Thick-Seam

To raise coal recovery ratio and solve the key problem of full-dimension mining, a new “3-section” technology of full-dimension mining is put forward in flat thick-seam according to the theory and mining practice. This technology is proved to be rational and feasible in rodway layout system of stagger arrangement.

Mining subsidence monitoring using the method of combining InSAR and GPS technology

Attempted to conduct a dynamic monitoring research on coal mining subsidence in western mining areas by using the method of combining D-InSAR and GPS technology. The observation points were installed on the main section and the three-dimensional coordinates of the points were measured by using the method of dynamic differential GPS. Meanwhile, the radar images of this subsidence area were processed by using the method of interferometry with daris software, and the interferogram of the subsidence area was obtained. Through this study, the GPS monitoring data and the InSAR deformation data were integrated and the dynamic subsidence contours of the experimental area were obtained. GPS/InSAR fusion technology provides a new technological means for large-scale dynamic monitoring of coal mining subsidence in western mountainous mining areas and shows good application prospects in coal mining subsidence monitoring and disaster warning.

Failure mechanism and control technology of longwall coalface in large-cutting-height mining method

The stability control of longwall coalface is the key technology of large-cutting-height mining method.Therefore,a systematic study of the factors that affect coalface stability and its control technology is required in the development of large-cutting-height mining method in China. After the practical field observation and years of study,it was found that the more than 95% of failures in coalface are shear failure. The shear failure analysis model of coalface has been established,that can perform systematic study among factors such as mining height,coal mass strength,roof load,support resistance,and face flipper protecting plate horizontal force. Meanwhile,sensitivity analysis of factors influencing coalface stability showed that improving support capacity,cohesion of coal mass and decreasing roof load of coalface are the key to improve coalface stability. Numerical simulation of the factors affecting coalface stability has been performed using UDEC software and the results are consistent with the theoretical analysis. The coalface reinforcement technology of large-cutting-height mining method using the grouting combined with coir rope is presented. Laboratory tests have been carried out to verify its reinforcement effect and practical application has been implemented in several coal mines with good results.It has now become the main technology to reduce longwall coalface failure of large-cutting-height mining method.

Fractal characterization for the mining crack evolution process of overlying strata based on microseismic monitoring technology

In order to study the evolution laws during the development process of the coal face overburden rock mining-induced fissure,we studied the process of evolution of overburden rock mining-induced fissures and dynamically quantitatively described its fractal laws,based on the high-precision microseismic monitoring method and the nonlinear Fractal Geometry Theory.The results show that:the overburden rock mining-induced fissure fractal dimension experiences two periodic change processes with the coal face advance,namely a Small→ Big→ Small process,which tends to be stable;the functional relationship between the extraction step distance and the overburden rock mining-induced fissure fractal dimension is a cubic curve.The results suggest that the fractal dimension reflects the evolution characteristics of the overburden rock mining-induced fissure,which can be used as an evaluation index of the stability of the overburden rock strata,and it provides theoretical guidance for stability analysis of the overburden rock strata,goaf roof control and the support movements in the mining face.