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A Web Semantic mining Method for Fake Cybersecurity Threat Intelligence in Open Source Communities

INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 2024年第1期20卷 1页

作者： Li, Zhihua Yu, Xinye Zhao, Yukai Jiangnan Univ Wuxi Peoples R China

In order to overcome the challenges of inadequate classification accuracy in existing fake cybersecurity threat intelligence mining methods and the lack of high-quality public datasets for training classification models, we propose a novel approach that significantly advances the field. We improved the attention mechanism and designed a generative adversarial network based on the improved attention mechanism to generate fake cybersecurity threat intelligence. Additionally, we refine text tokenization techniques and design a detection model to detect fake cybersecurity threats intelligence. Using our STIX-CTIs dataset, our method achieves a remarkable accuracy of 96.1%, outperforming current text classification models. Through the utilization of our generated fake cybersecurity threat intelligence, we successfully mimic data poisoning attacks within open-source communities. When paired with our detection model, this research not only improves detection accuracy but also provides a powerful tool for enhancing the security and integrity of open-source ecosystems.

关键词： Cybersecurity Threat Intelligence Fake Threat Intelligence Generation data mining algorithm

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Research on 3D medical image surface reconstruction based on data mining and machine learning

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第8期37卷 4654-4669页

作者： Hua, Shanshan Liu, Qi Yin, Guanxiang Guan, Xiaohui Jiang, Nan Zhang, Yuejin East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China Nanchang Univ Natl Engn Res Ctr Bioengn Drugs & Technol Nanchang Jiangxi Peoples R China

Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

关键词： 3D medical images data mining algorithm image surface reconstruction machine learning algorithm

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Legal Forensics System Based on data mining

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Procedia Computer Science 2024年 243卷 1089-1094页

作者： Hongrui Dai Law school of Chongqing University Chongqing 400044 China Law school of Yunnan Minzu University Kunming 650504 China

The existing forensics systems are developing in a more and more perfect direction in terms of forensic steps, legal constraints, audit supervision, etc., but they are still not fully applicable to legal forensics. A general legal forensics system must have a complete forensics process to ensure the smooth progress of the forensics work. One of the purposes of legal forensics is to use legal means to bring criminals to justice. Only by ensuring the legality of the evidence can it be accurately identified case, and therefore also to ensure the legality of the evidence collection process. In order to design a more powerful legal forensics system, this paper introduces a data mining algorithm to extract valuable information from massive data through association rules to improve the efficiency of forensics. At the same time, this paper also proposes three coding schemes for the integrity of the evidence object, and compares the forensic effects of the three schemes.

关键词： Legal Forensics data mining algorithm Association Rules Coding Scheme

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Research on Intelligent System of Remote Cooperation Information Sharing in Human Resource Management Based on Wireless Communication Technology 3

Research on Intelligent System of Remote Cooperation Informa...

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3rd IEEE International Conference on Electrical Engineering, Big data and algorithms, EEBDA 2024

作者： Zhang, Hong Tang, Wen Liu, Hongwei Liu, Siyuan School of Management Liaoning Communication University Shenyang110136 China School of Arts and Media Shenyang Institute of Technology Liaoning Shenyang113122 China Shenyang Research Institute of Chemical Industry Shenyang110020 China

ISBN: (纸本)9798350380989

Combining Delphi's development method, a HR management system suitable for small and medium-sized enterprises is designed and implemented. According to the actual needs of enterprises, the system is divided into four functional modules, and the data and information flow of each module are designed in detail. The SQL Server 2008 is constructed for this system. The entity, attribute, relationship, connection mode and constraint conditions based on rules are defined, and the personnel information base of the enterprise is constructed, so as to achieve the purpose of personnel management of enterprises. data mining algorithm is introduced to standardize the system documents, standardize the database and connect the database. The scientific decision of personnel scheduling was made. Then the method of data mining is introduced to develop a HR management system suitable for enterprises in order to promote the sustainable development of domestic health undertakings. The effectiveness of this scheme is verified by comparison experiments. The system can realize hundreds of people online at the same time, and there will be no abnormal conditions such as stuttering and flickering. © 2024 IEEE.

关键词： data mining algorithm Human resources remote collaboration scheduling decision system

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Construction of an Intelligent Mental Health Assessment System Based on Big data algorithms

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Procedia Computer Science 2024年 247卷 1102-1109页

作者： Fangrui Men Digital Marketing Industry College Shandong Institute of Commerce and Technology Jinan 250103 Shandong China

College students are in the stage of transitioning from teenagers to adults, and there are many problems that need to be adapted and adjusted in life, learning, and interacting with others. If these issues are not handled properly, it would lead to many mental health problems that affect normal learning and life. The paper use the data mining algorithm of Big data. Through systematic intelligent evaluation, it can gain an overall understanding of the mental status of students. For students, it is effective to have a clear understanding of their mental health status, and to communicate with their home and school in a timely manner regarding their psychological problems. Early treatment of mental health can be more effective in completing rehabilitation; For universities, they can have a clear understanding of each student's psychological situation to avoid some difficult situations. At the same time, universities can timely intervene and treat students with psychological problems. This paper use data mining algorithms to build an intelligent evaluation system for mental health. Through research, it is found that the accuracy of this method is 95% at the lowest and 97% at the highest. This method can comprehensively and objectively describe the changing characteristics of college students’ mental health, and the results of mental health intelligence assessment are stable, providing valuable reference for their mental health management.

关键词： Big data Technology data mining algorithm Mental Health Intelligent Evaluation System

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Neural Network Prediction of CFG Pile Composite Foundation Bearing Capacity based on BP algorithm

Neural Network Prediction of CFG Pile Composite Foundation B...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Caifeng, Jia Baicheng Vocational and Technical College Jilin Province 137000 China

ISBN: (纸本)9798350374933

In the rapid development of neural network prediction today, the use of big data algorithm means to improve the CFG pile composite foundation bearing capacity has become a trend, in order to be more adaptive to today's development, so that the CFG pile composite foundation bearing capacity to improve the neural network prediction comes along. In the neural network prediction, it is very important to collect and analyses the CFG pile composite foundation bearing capacity. data mining algorithms cannot accurately collect and analyse the accuracy problem in neural network prediction. As a result, this paper proposes a BP algorithm for data collection and analysis. Firstly, the indicators are divided according to the data collection requirements to reduce the interference factors in data collection. Then, the BP algorithm for neural network prediction data collection results, the formation of the data collection scheme, and the comprehensive analysis of the data collection results. MATLAB simulation shows that in the case of a certain evaluation standard, the BP algorithm for neural network prediction of the data collection accuracy, data collection reliability is better than the data mining algorithm. © 2024 IEEE.

关键词： BP algorithm CFG pile composite foundation bearing capacity data mining algorithm neural network prediction neural network prediction data collection

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SKDStream: a dynamic clustering algorithm on time-decaying data stream

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2022年第1期2022卷 102-102页

作者： Liu, Hui Wu, Aihua Wei, Mingkang Chang, Chin-Chen Shanghai Maritime Univ Dept Engn Shanghai Peoples R China Feng Chia Univ Dept Informat Engn & Comp Sci Taichung Taiwan

data stream is a type of data that continue to grow over time. For example, network security data stream will constantly be generated in the field of data security, and encrypted data stream will be generated in the privacy protection scenario. Clustering is a basic task in the analysis of data stream. In addition to the large amount of data and limited computer memory, there are the following challenges in time-decaying data stream clustering: (1) How to quickly process time-varying data stream and how to quickly save vaild data. (2) How to maintain and update clusters and track their evolution in real time. Based on the fact that the existing data stream algorithms do not provide a good strategy to the above problems, this paper proposes a dynamic clustering algorithm named SKDStream. The algorithm divides the entire data space into distinct minimal bound hypercubes, which are maintained and indexed by a newly defined structure, SKDTree, that aggregates and updates clusters in real time without requiring large primary storage. Clusters are composed of dense hypercubes. Experiments on synthetic datasets and real datasets show that the response time of the algorithm is similar to that of existing dataflow algorithms, but the quality of the generated clusters is relatively stable over time. Furthermore, the SKDStream algorithm is able to track the evolution of the number of clusters, centers, and density in real time, and compared to D-stream, SKDStream is efficient and effective in clustering.

关键词： data mining algorithm Density-based clustering Decaying stream High density subspace

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A Comparison of Methods of data mining algorithms Directed Predictive Pharmacosafety Networks for Adverse Drug Event Detection 16

A Comparison of Methods of Data Mining Algorithms Directed P...

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16th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Ji, Xiangmin Hua, Liyan Wang, Xueying Zhang, Yunfei Li, Jin Harbin Engn Univ Coll Automat Harbin Heilongjiang Peoples R China Ordos Inst Technol Dept Informat Engn Ordos Inner Mongolia Peoples R China

ISBN: (纸本)9781728116976

Adverse drug events (ADEs) detection is the critical concern in the field of pharmacovigilance, and it is also necessary to optimize the ADEs prediction to reduce the drugrelated morbidity and mortality. Here we propose a novel methods of data mining algorithms directed predictive pharmacosafety networks (PPNs) to compare their predictive performance and investigate the differences between data mining algorithms. The combinations of 152 cancer drugs and 633 ADEs in the 2010 FDA Adverse Event Reporting System(FAERS) data is the training data, and 2011-2015 FAERS data is the validation data. We find that performance of empirical Bayes geometric mean (EBGM) is closer to proportional reporting ratio (PRR), and greater than reporting odds ratio (ROR) in ADE detection. Further, only information component (IC) directed the PPNs have better predictive performance comparing to other data mining algorithms, the predictive performance of which reaches to AUROC=0.908 comparing to the existing AUROC=0.823, and the performance of IC is greater than EBGM in ADE detection.

关键词： Adverse drug event data mining algorithm Disproportionality Analysis Predictive Pharmacosafety Networks Pharmacovigilance

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RETRACTED: Research on economic function data and entrepreneurship analysis based on machine learning and computer interaction platform (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5635-5647页

作者： Nie, Xueling Univ Sci & Technol Liaoning Sch Business Adm Anshan Peoples R China

Big data has the characteristics of rapid data flow, massive data scale, dynamic data system, and various data types, and it has become increasingly apparent in improving innovation and entrepreneurship data analysis, trend prediction, and decision support. In this paper, the authors analyze the economic function data and entrepreneurship analysis based on machine learning. The support vector pair is very sensitive to the choice of parameters, and the parameters obtained using the genetic algorithm will greatly improve the accuracy of the model prediction. When using the genetic algorithm to find parameters, the cv method is used for verification. By applying big data technologies and platforms, it can provide strong data support to establish entrepreneurship education;integrate and integrate various types of innovation and entrepreneurship data, improve the quality of data *** the same time, through big data mining and analysis, accurately determine market demand hotspots and innovation and entrepreneurship trends, and promote scientific planning of innovation and entrepreneurship strategies. The research results show that this research model can be applied to actual projects in the future, and help investors better understand the changes of market economy.

关键词： Machine learning data mining algorithm driving innovation entrepreneurship education

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Detection of broiler heat stress by using the generalised sequential pattern algorithm

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BIOSYSTEMS ENGINEERING 2020年 199卷 121-126页

作者： Branco, Tatiane Moura, Daniella J. Naas, Irenilza A. Oliveira, Stanley R. M. Univ Estadual Campinas Coll Agr Engn Campinas SP Brazil Embrapa Agr Informat Campinas SP Brazil

The sequence pattern mining method aims to identify frequent sequences that exceed a user-specified support threshold. The present study uses the same approach based on sequential standards to estimate the heat stress of broilers from a resulting behavioural pattern. Experimental data were recorded in a climate chamber where the behaviour of broilers was recorded under thermoneutral (comfort) conditions, set as standard, and when exposed to thermal stress (cold and heat). The Generalised Sequential Patterns (GSP) algorithm was used to evaluate the heat stress of broilers in the third and fourth week of growth. The results indicated that the mining of pattern sequences is a useful and straightforward technique to estimate the welfare of broiler chickens, allowing the identification of temporal relations between thermal stress and the consequent behaviour of the broiler. Temperature 8 degrees C below the standard thermoneutral conditions showed that the broiler remained lying down most of the time, walking only to the drinker and feeder trough. Broilers exposed to temperatures 8 degrees C above the standard thermoneutral conditions () tend to decrease locomotor activities, showing lower welfare status. (C) 2019 Published by Elsevier Ltd on behalf of IAgrE.

关键词： Animal welfare Behavioural pattern data mining algorithm Detection of sequential frequency

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