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Adaptive Model for Protection of Electronic Resources against information Security Threats 15

Adaptive Model for Protection of Electronic Resources agains...

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15th International Scientific and Practical Conference on Environment. Technology. Resources, ETR 2024

作者： Kerimov, Komil Azizova, Zarina Department of System and Applied Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Information Security Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781713899570

The rapid development of digitalization and the creation of electronic resources, in areas such as e-commerce, government portals and others leads to the actualization of data protection issues. The protection of electronic resources is becoming more and more relevant every day. This article presents the concept of adaptive protection of electronic resources from information security threats. In the course of this research, an adaptive model of protection of electronic resources from threats to information security based on behavioral analysis was developed. © 2024 Komil Kerimov, Zarina Azizova.

关键词： Security of data

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Methodology of information Security Risk Assessment of Electronic Resources under Unauthorized Access Threats 15

Methodology of Information Security Risk Assessment of Elect...

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15th International Scientific and Practical Conference on Environment. Technology. Resources, ETR 2024

作者： Kerimov, Komil Azizova, Zarina Department of System and Applied Programming Tashkent University of Information Technologies Named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Information Security Tashkent University of Information Technologies Named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781713899570

The article proposes a methodology for assessing the risk of information security of a computer network based on the results of the analysis of vulnerability attributes and protection attributes of information system elements, as well as security attributes of information system elements. According to the results of the research the space of information protection signs is formed. The results of the analysis of possible variants of threats of unauthorized access to electronic resources of the computer network, as well as solutions to reduce the risks of information security are given. Quantitative indicators of the results of the application of the proposed methodology to assess the risk of threats of unauthorized access to electronic resources of the computer network confirm the effectiveness of the proposed methodology, which can be used to improve the level of protection of electronic resources in organizations. © 2024 Komil Kerimov, Zarina Azizova.

关键词： Computer networks

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An Algorithm for Decentralized Multi-agent Feasibility Problems 22nd

An Algorithm for Decentralized Multi-agent Feasibility Prob...

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22nd International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2023

作者： Pinyagina, Olga Department of Data Mining and Programming Technologies Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan Russia

ISBN: (纸本)9783031432569

We consider the feasibility problem in a multi-agent decentralized form, where each agent has the personal information of the feasible subset, which is unknown to other agents. The common feasible set is composed of the agents’ feasible subsets. For solving this problem, we reformulate it in the form of a variational inequality and propose an algorithm based on the projection method. Preliminary test calculations confirm the efficiency of the proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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Ensuring information Security in the Intelligent Scientific and Technical information Systems 15

Ensuring Information Security in the Intelligent Scientific ...

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15th International Scientific and Practical Conference on Environment. Technology. Resources, ETR 2024

作者： Kerimov, Komil Azizova, Zarina Bekkamov, Fayzi Rakhimov, Mekhriddin Ochilov, Mannon Department of System and Applied Programming Tashkent University of Information Technologies Named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Information Security Tashkent University of Information Technologies Named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Library Information Systems Tashkent University of Information Technologies Named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Computer Systems Tashkent University of Information Technologies Named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781713899570

Scientific and scientific-technical information is a valuable tool for the development of education, technology and society as a whole. The increase in the volume of information and the development of information networks of data exchange requires special means to ensure information protection of data. Methods, means and systems for information security of scientific, technical and scientific-educational resources in intellectual information systems are of particular importance. The purpose of the research is to develop methods and software tools to ensure information security of valuable scientific and technical information resources in intelligent information systems. The proposed solution for intrusion detection in intelligent system is a web application firewall, which is used for enhanced security, detecting and preventing attacks before they reach the web application. It will protect the system from a whole range of attacks while allowing HTTP traffic monitoring and analyzing small changes or persistent state online. The Web Application Firewall (WAF) has the following features: logging of all HTTP protocol transactions, including request termination permissions and logging of the response;HTTP traffic can be examined in real time to detect attacks;preventing attacks before they reach the web application. This work is performed within the framework of the project on creation of an integrated intelligent system "SMART TUIT", which includes several subsystems (information Retrieval, Voice Recognition, Pattern Recognition, Scientific information Assessment, Geoinformation System). © 2024 Komil Kerimov, Zarina Azizova, Fayzi Bekkamov, Mekhriddin Rakhimov, Mannon Ochilov.

关键词： Intelligent systems

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Classification Algorithms with a Complex Structure in Pattern Recognition 25

Classification Algorithms with a Complex Structure in Patter...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Nishanov, Akram Kh. Allamov, Oybek Rakhmanov, Askar T. Yusupova, Janar Ruzibaev, Ortik B. Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al Khwarizmi Urgench Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Faculty of Software Engineering Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9798350389234

The article considers in the intellectual processing of information, moving from a large unit of measurement to a smaller, more important unit of measurement, selecting sets of informative symbols and classifying symbols, developing modern recognition systems that help specialists in the field, as well as evaluating the states of objects to be classified, developing a classification algorithm for multi-class cases of educational samples. The development of a multi-class algorithm for symbol classification serves to develop the theory of symbol recognition. It deals with the construction of a decision rule construction algorithm. Its main purpose is to determine which of the given classes the recognized object belongs to. The complexity or simplicity of the proposed algorithm depends on the number of classes participating in the study sample. If the number of classes is two, then the formulation of decision rules is simple. That is why the classification algorithms proposed by most authors are solved for cases where the number of classes is three or more, the decisive rule in the matter of classification of objects is solved by making two classes. The construction of a multi-class decision rule for classification, which takes into account all classes at the same time, is a complex and urgent issue. In the article, in the classification of objects, a multi-level complex structured decision rule was developed based on special inequalities, and two-, three- and multi-class classification algorithms were developed. © 2024 IEEE.

关键词： Consensus algorithm

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METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALGORITHM 5

METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALG...

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Gavrylenko, Svitlana Zozulia, Vladislav Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350384499

An analysis of modern computer network intrusion detection systems was carried out. The application of machine and deep learning methods for classification problems has been investigated. The UNSW-NB15 dataset, developed at the Australian Cyber Security Center's (ACCS) Cyber Range Laboratory, contains data on normal network operations and synthetic intrusions. Data pre-processing was performed, including class balancing using the SMOTEENN method and selection of informative features using the Recursive Feature Elimination method. The possibility of using the stacking meta-algorithm to detect intrusions into computer networks has been investigated. A new algorithm for generating packets of raw data is proposed, which generates two sets of training data: one for training basic models, the other for a meta-model. A study of the effectiveness of using Random Forest, ANN, K Nearest Neighbor methods and Support Vector Machine and Random Forest as a decision-making meta-model was conducted. The use of the stacking meta-algorithm with the proposed algorithm for forming packets of output data, as well as basic models and a meta-model, led to a significant improvement in the quality of the model. It was found that, on average, recall and f1 score increased by 55.6% and 37.4%, respectively, compared to raw data and other models. ©2024 IEEE.

关键词： Network intrusion

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A Case Study on Data Protection for a Cloud- and AI-Based Homecare Medical Device 43rd

A Case Study on Data Protection for a Cloud- and AI-Based...

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43rd International Conference on Application and Theory of Petri Nets and Concurrency , Petri Nets 2022

作者： Bende, Philipp Vovk, Olga Caraveo, David Pechmann, Ludwig Leucker, Martin Institute for Software Engineering and Programming Languages University of Lübeck Lübeck Germany School of Information Technologies Department of Health Technologies Tallinn University of Technology Tallinn Estonia UniTransferKlinik Lübeck GmbH Lübeck Germany

ISBN: (纸本)9783662681909

To improve the treatment of many diseases, continuous monitoring of the patient at home with the ability of doctors to interact with individual cases demands an increasing number of medical devices connected to the cloud. To support the doctor’s duties, such devices may benefit from AI-based diagnosis routines. In order for such devices to be approved and placed on the market, they need to comply with various legal, regulatory, economic, and social requirements. An integral part of these requirements is the protection of the patients’ data. In this paper, based on a current use case, we describe a workflow on how to identify risks and address their mitigations. To this end, we recall the relevant legal, regulatory, economic, and social data protection requirements. We pursue our findings on a Homecare OCT device that is intended to be used by elderly patients on a daily basis, by taking images of their eyes and sending them for further analysis to a cloud- and AI-based system. The patient’s ophthalmologist gets notified for further dedicated treatment depending on the result. We then compare the Homecare OCT device with a clinical OCT System in regard to various risks to patient data which arise when a medical system is used outside of a secure hospital environment. To perform the risk management, we describe (i) the architecture of both systems, (ii) analyze their data flow, (iii) discuss several vectors of attack, (iv) propose ways to mitigate the risks, and (v) discuss the handling of potential data breaches. © 2024, The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature.

关键词： Decision support systems

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METHODS FOR IMPROVING THE QUALITY OF CLASSIFICATION ON IMBALANCED DATA 4

METHODS FOR IMPROVING THE QUALITY OF CLASSIFICATION ON IMBAL...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Gavrylenko, Svitlana Vladislav, Zozulia Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350395532

The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate methods of balancing classes and to develop a classification method on imbalanced data to increase the level of network security. The methods used are: methods of artificial intelligence, machine learning. The following results were obtained: Class balancing methods based on Undersampling, Oversampling and their combinations were studied. The following methods were chosen for further research: SMOTEENN, SVMSMOTE, BorderlineSMOTE, ADASYN, SMOTE, KMeansSMOTE. The UNSW-NB 15 set was used as the source data, which contains information about the normal functioning of the network and during intrusions. A decision tree based on the CART (Classification And Regression Tree) algorithm was used as the basic classifier. According to the research results, it was found that the use of the SMOTEENN method provides an opportunity to improve the quality of detection of intrusions in the functioning of the network. Conclusions. The scientific novelty of the obtained results lies in the complex use of data balancing methods and the method of data classification based on decision trees to detect intrusions into computer networks, which made it possible to reduce the number of Type II errors. © 2023 IEEE.

关键词： Classification (of information)

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AI-based Power Transformer Condition Assessment

AI-based Power Transformer Condition Assessment

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2022 International Conference on information Science and Communications technologies, ICISCT 2022

作者： Rakhimov, Mekhriddin Abdurakhmanov, Dias Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Artificial Intelligence Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of System and Application Programming Tashkent Uzbekistan

ISBN: (纸本)9781665472296

Diagnostics of electrical equipment today is one of the main 'devices' for assessing its technical condition and allows you to predict its service life (residual resource), which is an a priori task for such strategically important facilities as power transformer. Over the past decade, the understanding of the economic feasibility of technical diagnostics and condition assessment of electrical equipment has increased for many reasons. Therefore, determining which transformers require the most attention can be an extremely difficult task. Condition assessment and data analysis based on artificial intelligence is of great importance for improving the completeness, efficiency and accuracy of the state assessment. Artificial intelligence technologies, such as machine learning, are among the most effective that can be used to solve the problems of assessing the condition of power transformers. K-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve a binary classification. This paper presents the selection of important technical indicators of the transformer and the implementation of the KNN algorithm that aims to improve the accuracy of the binary classification. © 2022 IEEE.

关键词： Power transformers

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Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning 16

Artificial Intelligence in Medicine for Chronic Disease Clas...

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16th IEEE International Conference on Application of information and Communication technologies, AICT 2022

作者： Rakhimov, Mekhriddin Akhmadjonov, Ravshanjon Javliev, Shahzod Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Artificial Intelligence Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of System and Application Programming Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Computer System Tashkent Uzbekistan

ISBN: (纸本)9781665451628

Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification. © 2022 IEEE.

关键词： Diseases

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