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RESEARCH ON DATA SECURITY DETECTION algorithm IN IOT BASED ON K-MEANS

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2021年第2期22卷 149-159页

作者： Zhu, Jianxing Huo, Lina Ansari, Mohd Dilshad Ikbal, Mohammad Asif XingTai Univ Coll Math & Informat Technol Xingtai 054001 Peoples R China CMR Coll Engn & Technol Dept Comp Sci & Engn Hyderabad India Lovely Profess Univ Dept Elect Engn Phagwara Punjab India

The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method Aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks, the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods;the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. This paper proposes an efficient intrusion detection method for applications with Internet of Things.

关键词： Internet of things intrusion detection clustering algorithm network security

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Design of Computer Network Security Intrusion Prevention Strategy and Evaluation algorithm Analysis Technology

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Procedia Computer Science 2023年 228卷 1270-1276页

作者： Fengzheng Wang Shandong Transport Vocational College Weifang Shandong China

With the continuous improvement of global informatization, computer networks have basically reached complete popularization. In today's society, it has become a major application and provides corresponding services for various industries. Therefore, it is necessary to effectively protect various information in the network. Therefore, this article provided a detailed discussion on the problems existing in computer network information security. These issues included their own reasons, hacker intrusion, and spam. Based on this, this article explored computer network information security protection strategies such as deploying data encryption, timely patching system vulnerabilities, and installing firewalls and antivirus software. The experimental results showed that the network output values of the clustering algorithm were: 0.888 for level 1, 0.725 for level 2, 0.678 for level 3, 0.461 for level 4, and 0.211 for level 5. These were all within the scope.

关键词： Intrusion Prevention Strategy Computer Network Security Evaluation algorithm Analysis clustering algorithm

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An Ensemble of Geostatistical Simulated Realizations Using a clustering algorithm to Reproduce Model-Based Statistics: Case Study of a Gravel Reserve at Kivalina, Alaska

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MARINE GEORESOURCES & GEOTECHNOLOGY 2013年第3期31卷 225-241页

作者： Chatterjee, Snehamoy Bandopadhyay, Sukumar Natl Inst Technol Dept Min Engn Rourkela India Univ Alaska Fairbanks Coll Engn & Mines Fairbanks AK USA

This paper demonstrates the convergence of model-based statistics from multiple simulated realizations. Theoretically, the convergence of realization statistics is guaranteed over the number of realizations that are independent among themselves. The rate at which realization-based statistics converges with model-based statistics is important and must be assessed. However, due to poor selection of the random number generator, the generated realization might be far from mutual independence. We use the k-means clustering algorithm to select nearly independent realizations from a set of realization models. We apply the proposed algorithm to a coastal erosion problem in Alaska to estimate the amount of gravel.

关键词： clustering algorithm geostatistics gravel reserve Kivalina Alaska

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The potential of Wi-Fi data to estimate bus passenger mobility

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2023年 192卷

作者： Lea, Fabre Caroline, Bayart Bonnel, Patrick Mony, Nicolas Univ Lumiere Lyon 2 Lab Amenagement Econ Transports 3 Rue Maurice Audin F-69120 Vaulx En Velin France Univ Lumiere Lyon 1 Lab Sci Actuarielles & Financieres 50 Ave Tony Garnier F-69007 Lyon France Explain 36 Bd Canuts F-69004 Lyon France

Last decades have been marked by deep socio-economic transformations, an uneven evolution of transport demand in main urban areas and the emergence of new and more sustainable modes of transportation (carpooling, self-services bicycles). These changes have strongly impacted the interaction between service supply and demand in the transport industry. In this context, passive data as Wi-Fi and Bluetooth become a key source of information to understand individual mobility behaviors and ensure the sustainable development of transport infrastructures. In this paper, we present a framework that uses disruptive technology to collect passive data in buses, continuously and at a lower cost than traditional mobility surveys. Previous research, conducted over a more limited spatial and temporal framework, uses filtering methods, which do not allow the results to be replicated. This study uses artificial intelligence to sort transmitted signals, get transit ridership and build Origin-Destination matrices. Its originality consists in providing a concrete, automatic and replicable method to transport operators. The comparison of the results with other data sources confirms the relevance of the presented algorithms in demand forecasting. Therefore, our findings provide interesting insights for data-driven decision making and service quality management in urban public transport.

关键词： Passive data analytics Wi-Fi sensors clustering algorithm Origin-Destination matrices Travel behavior Public transport demand

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A Text Mining Model Based on Improved Density clustering algorithm

A Text Mining Model Based on Improved Density Clustering Alg...

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4th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Chen Qi Lu Jianfeng Zhang Hao Tongji Univ CIMS Ctr Shanghai 200092 Peoples R China

ISBN: (纸本)9781467349338

The clustering algorithm based on density is widely used on text mining model, for example the DBSCAN(density-based spatial clustering of application with noise) algorithm. DBSCAN algorithm is sensitive in choose of parameters, it is hard to find suitable parameters. In this paper a method based on k-means algorithm is introduced to estimate the E neighborhood and minpts. Finally an example is given to show the effectiveness of this algorithm.

关键词： clustering algorithm text mining density clustering

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ESC: An efficient synchronization-based clustering algorithm

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KNOWLEDGE-BASED SYSTEMS 2013年 40卷 111-122页

作者： Huang, Jianbin Sun, Heli Kang, Jianmei Qi, Junjie Deng, Hongbo Song, Qinbao Xidian Univ Sch Software Xian 710071 Peoples R China Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian 710049 Peoples R China Univ Illinois Dept Comp Sci Urbana IL 61801 USA

clustering is an essential approach for detecting the intrinsic groups in data. An efficient clustering algorithm based on a generalized local synchronization model is proposed. It uses a novel stopping criterion of data synchronization to detect clusters prior to the perfect synchronization. Moreover, a density-biased sampling method is adopted to extract samples from the original data set. The clustering structure can be effectively revealed on the samples. As a result, the clustering efficiency is significantly improved. By using a cluster validity criterion, the proposed algorithm can find clusters of arbitrary number, shape, size and density as well as isolate noises in the vector data without any data distribution assumption. Extensive experiments on several synthetic and real-world data sets show that the proposed algorithm possesses high accuracy and it is more efficient than the state-of-the-art synchronization-based clustering method. (C) 2012 Elsevier B.V. All rights reserved.

关键词： clustering algorithm Dynamical synchronization model Neighborhood closure Density-biased sampling Cluster validity criterion

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Anomaly Detection in Student Activity in Solving Unique Programming Exercises: Motivated Students against Suspicious Ones

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DATA 2023年第8期8卷

作者： Demidova, Liliya A. Sovietov, Peter N. Andrianova, Elena G. Demidova, Anna A. MIREA Russian Technol Univ Fed State Budget Educ Inst Higher Educ Inst Informat Technol 78 Vernadsky Ave Moscow 119454 Russia

This article presents a dataset containing messages from the Digital Teaching Assistant (DTA) system, which records the results from the automatic verification of students' solutions to unique programming exercises of 11 various types. These results are automatically generated by the system, which automates a massive Python programming course at MIREA-Russian Technological University (RTU MIREA). The DTA system is trained to distinguish between approaches to solve programming exercises, as well as to identify correct and incorrect solutions, using intelligent algorithms responsible for analyzing the source code in the DTA system using vector representations of programs based on Markov chains, calculating pairwise Jensen-Shannon distances for programs and using a hierarchical clustering algorithm to detect high-level approaches used by students in solving unique programming exercises. In the process of learning, each student must correctly solve 11 unique exercises in order to receive admission to the intermediate certification in the form of a test. In addition, a motivated student may try to find additional approaches to solve exercises they have already solved. At the same time, not all students are able or willing to solve the 11 unique exercises proposed to them;some will resort to outside help in solving all or part of the exercises. Since all information about the interactions of the students with the DTA system is recorded, it is possible to identify different types of students. First of all, the students can be classified into 2 classes: those who failed to solve 11 exercises and those who received admission to the intermediate certification in the form of a test, having solved the 11 unique exercises correctly. However, it is possible to identify classes of typical, motivated and suspicious students among the latter group based on the proposed dataset. The proposed dataset can be used to develop regression models that will predict outbursts of student activ

关键词： unique programming exercises tasks Python Digital Teaching Assistant anomalies detection typical students motivated students suspicious students clustering algorithm UMAP algorithm

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A clustering algorithm-based control chart for inhomogeneously distributed TFT-LCD processes

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第18期51卷 5644-5657页

作者： Kang, Ji Hoon Kim, Seoung Bum Korea Univ Sch Ind Management Engn Seoul South Korea

Statistical process control techniques have been widely used to improve processes by reducing variations and defects. In the present paper, we propose a multivariate control chart technique based on a clustering algorithm that can effectively handle a situation in which the distribution of in-control observations is inhomogeneous. A simulation study was conducted to examine the characteristics of the proposed control chart and to compare them with Hotelling's T-2 multivariate control charts that are widely used in real-world processes. Moreover, an experiment with real data from the thin film transistor liquid crystal display (TFT-LCD) manufacturing process demonstrated the effectiveness and accuracy of the proposed control chart.

关键词： bootstrap clustering algorithm Hotelling's T-2 multivariate control chart TFT-LCD

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Identifying spatiotemporal propagation of droughts in the agro-pastoral ecotone of northern China with long-term WRF simulations

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AGRICULTURAL AND FOREST METEOROLOGY 2023年 336卷

作者： Wang, Xuejin Zhang, Baoqing Zhang, Zhenyu Tian, Lei Kunstmann, Harald He, Chansheng Lanzhou Univ Coll Earth & Environm Sci Key Lab West Chinas Environm Syst Minist Educ Lanzhou 730000 Gansu Peoples R China Western Michigan Univ Dept Geog Environm & Tourism Kalamazoo MI 49008 USA Lanzhou Univ Inst Green Dev Yellow River Drainage Basin Lanzhou 730000 Gansu Peoples R China Karlsruhe Inst Technol Inst Meteorol & Climate Res IMK IFU Campus Alpin Garmisch Partenkirchen Germany Univ Augsburg Inst Geog Augsburg Germany

Understanding the spatiotemporal evolution of droughts is critical for food security and water allocation. In this study, we propose an approach to construct the linkage of the propagation from meteorological drought to agricultural drought to explore the simultaneous spatiotemporal evolution of droughts based on a 3-dimensional clustering algorithm. We first evaluate the improvement of the downscaled high-resolution outputs from the Weather Research and Forecasting (WRF) model to reanalysis data in detecting wet-dry variations in the agro-pastoral ecotone of northern China (APENC). The WRF simulation results well characterize the evolution of droughts with a high correlation coefficient of 0.82 (p<0.05), which implies that the WRF downscaling model provides reliable hydrometeorological results for assessing and analyzing long-term wet-dry variations. Subse-quently, we identify and track the spatiotemporal evolution of each individual drought event in the APENC for the first time. Based on the standardized precipitation index calculated from the WRF outputs, a total of 185 drought clusters were identified during 2000-2017, with 28 drought events lasting at least 3 months duration occurring in the APENC. The characteristics of the different drought events vary considerably. Droughts in the APENC have a larger percentage of propagation to the northeast and northwest and migrated hundreds of ki-lometers from the source sites. The results of the propagation of meteorological droughts to agricultural droughts indicate that 53.75% of the meteorological drought events progressed further to agricultural droughts. Agri-cultural droughts exhibit spatial variations consistent with meteorological droughts. Agricultural droughts have 1.69 months onset lag time, 2.12 months termination lag time, and overall longer duration than meteorological droughts in the APENC. Our findings provide valuable information for the mechanism and prediction of drought propagation.

关键词： Drought propagation Spatiotemporal continuity clustering algorithm Agro-pastoral ecotone of northern WRF

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Research on Traffic Classification for Space-Ground Integrated Network Based on Feature clustering 2021

Research on Traffic Classification for Space-Ground Integrat...

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2021 The 9th International Conference on Information Technology: IoT and Smart City

作者： Lei Gu Zhongliang Deng Yicheng Liao Ke Wang Wenliang Lin Xiaoyi Yu Hao Liu Yang Liu Xiangfeng Kong School of Electronic Engineering Beijing University of Posts and Telecommunications China School of Information and Communication Engineering Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450384971

The space-ground integrated information network takes the space-based network as the main body, the ground network as the foundation and according to the type of service, it can access multiple users in real time and realize the communication of a large number of users. With the development of satellite communication and the popularization of 5G networks, the space-ground integrated network has received more and more attention. However, due to problems such as strong traffic bursts and time-varying topologies in the space-ground integrated information network, communication is easily interrupted. In order to make the traffic prediction more accurate, based on the feature clustering, this paper studies the traffic identification of the space-ground integrated network, and designs a traffic sensing algorithm based on the density peak clustering. According to traffic characteristics and applicable scenarios, analyze and process traffic perception from service and functional perspective. According to the functional requirements, this paper design the overall framework of the clustering algorithm. Finally, realize and compare the algorithm on OPNET simulation platform.

关键词： space-ground integrated information network clustering algorithm traffic awareness

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