In order to improve the efficiency of the software intrusion detection system, the author proposes an application based on data mining technology in software intrusion detection and information processing. Apply data mining technology to software intrusion detection; first, analyze and research software intrusion detection technology and data mining technology, including the basic concepts of software intrusion detection, the realization technology of software intrusion detection, the classification of software intrusion detection systems, and the typical software intrusion detection system situation. By detecting and analyzing known intrusion data and using association rules, constructing the inspection system rule base enables the system to learn independently and improve itself and has good scalability, while improving the degree of automation and complete intrusion detection. Experimental results show that under the same test sample, the accuracy of the detection system model designed in this paper is 95.67%, higher than the other three detection systems, and the false alarm rate is lower than other systems, which has certain advantages. It is proved that the system in this paper can help improve the accuracy of software intrusion detection, significantly reduce the false alarm rate and false alarm rate of software intrusion detection, and provide reference for the optimization and improvement of software intrusion detection system and information processing. The system has a certain degree of self-adaptation, which can effectively detect external intrusions.

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Application of Data Mining Technology in Software Intrusion Detection and Information Processing

In order to study the problems of information disconnection, poor coordination of project participants, design evaluation, and so on, in the process of green building evaluation in China, this paper proposes a construction method of green building integrated evaluation system based on BIM Technology. This method constructs the research framework through the research on green building evaluation and BIM (building information modeling), analyzes the whole life cycle of BIM in green building, solves the problems existing in green building evaluation in China through the application of BIM, expounds the green building evaluation process under BIM environment, and studies the information interaction of green building evaluation under BIM environment and the application of BIM in green building design evaluation and operation evaluation. The results show that by studying the application of BIM in green building evaluation, it is demonstrated that the green building evaluation based on BIM can effectively improve the evaluation efficiency and realize the informatization, integration, and high efficiency of green building evaluation.

The Construction of Green Building Integrated Evaluation System Based on BIM Technology

In order to solve the problem of the effect of CT images on the diagnosis of lungs, the authors proposed a method for the diagnosis of invasive mucinous adenocarcinoma of the lungs based on CT radiomic features, and the modified method is found by reviewing past cases: among the 34 cases of primary pulmonary lymphoma, 12 cases were nodular mass type, 19 cases were nonnodular mass type, and 3 cases were mixed type; 13 cases involved bilateral lung lobes, 7 cases involved right lung, and 4 cases involved left lung example. There were 17 cases of tumor consolidation density shadow, 17 cases of mixed density shadow, the average CT value was about 32HU, 15 cases of cavitation sign, 6 cases of cavity, 9 cases of angiography sign, 30 cases of air bronchus sign, 22 cases of bronchiectasis, bronchial stenosis or amputation in 8 cases, pleural effusion in 12 cases, lymph node enlargement in 15 cases, and pleural metastasis in 2 cases. The final pathological results included 24 cases of membrane-associated lymphoid tissue (MALT) lymphoma, 9 cases of diffuse large B-cell lymphoma (DLBCL), and 1 case of T-cell lymphoma. The CT manifestations of primary pulmonary lymphoma (PPL) are diverse and do not have obvious specificity, the imaging manifestations are correlated with pathological types, and air bronchial signs, bronchiectasis, angiography signs, and other signs are used for the diagnosis of PPL. This is of great significance for the diagnosis of PPL.

https://downloads.hindawi.com/journals/scanning/2022/5314225.pdf

Diagnostic Efficacy of CT Radiomic Features in Pulmonary Invasive Mucinous Adenocarcinoma

In order to solve the problems of low efficiency, high consumption of human and time resources, and low degree of intelligence in the current financial abnormal data detection system in computerized accounting, this paper proposes a financial abnormal data monitoring and analysis algorithm based on data mining and neural network. The analysis algorithm uses the method of data mining to process the original financial data, remove invalid information, retain valuable information, and standardize the data to solve the problem of large labor and time consumption. Then, neural network-related algorithms are used to identify the anomalies of standardized data, so as to realize the intelligent early warning of financial abnormal data. Compared with the financial audit algorithm in traditional accounting computerization, this algorithm has the advantages of high efficiency, low energy consumption, and high intelligence. The test results show that the classification accuracy of the proposed algorithm for abnormal data can reach more than 90%. It is proved that the algorithm is effective and improves the efficiency at the same time. The classification error rate of the classifier designed in this paper is 22.5%, and the accuracy rate is 77.5%. Both estimated and actual values represent the number of times, and there is no physical unit. The experiment shows that the main reason for the error rate is the delay of the inspection results of financial abnormalities. Through the example analysis, it can be concluded that the proposed intelligent analysis method of financial abnormal data based on deep learning has good effectiveness and accuracy and has a certain practical value.

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Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

In order to improve the certainty and clarity of information security detection, an application method of big data clustering algorithm in information security detection is proposed. The experimental results show that when the amount of data is close to 6000, the efficiency of the improved algorithm is nearly 70% higher than that of DBSCAN, and it is still very close to the efficiency of the BIRCH algorithm. The algorithm has a high processing speed for large-scale data sets without increasing the time complexity and can also accurately cluster clusters of any shape. When the data set increases from 9000 rows to 58000 rows, in turn, the time-consuming of the traditional DBSCAN algorithm increases sharply, while the time-consuming of the improved DBSCAN algorithm is still stable, and the time-consuming gap between the two is getting bigger and bigger. At the same time, the algorithm adopts a heuristic adaptive algorithm to estimate some threshold parameters of the clustering algorithm, which can avoid the direct setting of the threshold parameters by the user and can effectively estimate the relevant threshold parameters, extract clusters of any shape, and the clustering effect is obvious.

/pdf/dbscan-clustering-algorithm-based-on-big-data-is-applied-in-1qcwinbm.pdf

Siyang Zhang

Papers

Progress of zinc oxide-based nanocomposites in the textile industry