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IEEE Conference on Communications and Network Security (CNS)

作者： Andrea Corsini Shanchieh Jay Yang Department of Science and Methods for Engineering University of Modena and Reggio Emilia Modena Italy Department of Computer Engineering Rochester Institute of Technology Rochester USA

Machine learning (ML) has become increasingly popular in network intrusion detection. However, ML-based solutions always respond regardless of whether the input data reflects known patterns, a common issue across safety-critical applications. While several proposals exist for detecting Out-Of-Distribution (OOD) in other fields, it remains unclear whether these approaches can effectively identify new forms of intrusions for network security. New attacks, not necessarily affecting overall distributions, are not guaranteed to be clearly OOD as instead, images depicting new classes are in computer vision. In this work, we investigate whether existing OOD detectors from other fields allow the identification of unknown malicious traffic. We also explore whether more discriminative and semantically richer embedding spaces within models, such as those created with contrastive learning and multi-class tasks, benefit detection. Our investigation covers a set of six OOD techniques that employ different detection strategies. These techniques are applied to models trained in various ways and subsequently exposed to unknown malicious traffic from the same and different datasets (network environments). Our findings suggest that existing detectors can identify a consistent portion of new malicious traffic, and that improved embedding spaces enhance detection. We also demonstrate that simple combinations of certain detectors can identify almost 100% of malicious traffic in our tested scenarios.

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System Methodology of Data Analysis and Preprocessing for Solving Classification Problems

System Methodology of Data Analysis and Preprocessing for So...

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International computer sciences and Information Technologies (CSIT)

作者： Irina Kalinina Aleksandr Gozhyj Victoria Vysotska Eugene Malakhov Victor Gozhyj Iryna Tregubova Intelligent Information Systems Department Petro Mohyla Black Sea National University Mykolayiv Ukraine Department of Information Systems and Networks Institute of Computer Science and Information Technologies National University “Lviv Polytechnic” Lviv Ukraine Department of Mathematical Methods of Computer Systems of Odessa National Mechnikov University Odesa Ukraine Department of Computer Science State University of Intelligent Technologies and Telecommunications Odesa Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article describes and investigates the systematic methodology of data analysis and preprocessing for solving classification problems. The methodology combines the following groups of methods on the basis of a systemic approach: methods of processing data gaps, methods of processing anomalous values, methods of feature generation, methods of identifying nonlinearities and non-stationarity, and normalization methods. methods of character generation were studied. methods of feature selection and generation are divided into three main groups: filtering methods, wrapping methods, and embedded methods. Since feature selection plays a crucial role in machine learning, increasing model performance and reducing computational costs, the paper proposes a combined feature selection method that includes the step-by-step use of both filtering and wrapping methods. The method consists of five steps to efficiently select the most relevant features of a data set. It offers a better approach to feature selection. This results in improved model performance with fewer features and reduced computational cost. The creation of a red wine classification system was considered for the experimental verification of the system methodology of analysis and pre-processing of data. The Red Wine Quality dataset was used. The purpose of the classification is to identify factors associated with the risk of untimely delivery of previous orders to customers and information about recipients of goods. To solve the task of wine quality classification, simulations were carried out using various algorithms. The effectiveness of the system approach to solving problems of analysis and preprocessing of data to solve problems of classification was proved.

关键词： Data analysis Systematics Filtering Computational modeling Machine learning Feature extraction Computational efficiency Classification algorithms Wrapping Information technology

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Words and Action: Modeling Linguistic Leadership in #BlackLivesMatter Communities

arXiv

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arXiv 2024年

作者： Roytburg, Dani Olorunisola, Deborah Soni, Sandeep Klein, Lauren Emory University Department of Quantitative Theory & Methods United States Emory University Department of Computer Science United States Yale University Department of Data Science and Statistics United States Emory University Department of English United States

In the wake of the 2024 US presidential election, pundits on both the left and the right pointed to a conservative backlash against "woke politics" to explain the election’s outcome. These politics, rooted in substantive beliefs about equity and justice–and particularly racial justice–owe their most recent rise to prominence to the Black Lives Matter (BLM) movement. A significant body of work, both qualitative and quantitative, has documented how BLM was able to move these beliefs from the margin to the mainstream. In this paper, we focus on the words that index these beliefs, devising a novel method of modeling semantic leadership across a set of communities associated with the BLM movement that is informed by domain-specific theory about Black Twitter. We describe our bespoke approaches to time-binning, community clustering, and connecting communities over time, as well as our adaptation of state-of-the-art approaches to semantic change detection and semantic leadership induction. We find evidence at scale of the leadership role of BLM activists and progressives, as well as of Black celebrities. We also find evidence of sustained conservative engagement with this discourse, suggesting an alternative explanation for how we have arrived at the present political moment. © 2024, CC BY-NC-SA.

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Machine Learning for Energy Load Prediction and its Interpretation 11

Machine Learning for Energy Load Prediction and its Interpre...

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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Charytanowicz, Malgorzata Olwert, Anna Radziszewska, Weronika Jarnicka, Jolanta Gajowniczek, Krzysztof Zabkowski, Tomasz Brozyna, Jacek Mentel, Grzegorz Matejko, Grzegorz Research Institute Pas Center for Methods of Data Analysis Systems Warsaw Poland Research Institute Pas Department of Computer Science Systems Warsaw Poland Research Institute Pas Center for Computer Modelling Systems Warsaw Poland Warsaw University of Life Sciences Institute of Information Technology Warsaw Poland Rzeszow University of Technology Department of Quantitative Methods Rzeszów Poland Polskie Towarzystwo Cyfrowe Lublin Poland

ISBN: (纸本)9781665456562

This paper refers to real-life data on energy consumption from three companies, connected to a medium voltage grid. The companies use single-, two-, or three-zone tariffs and the general characteristic of their activity is comparable. The input dataset contains hourly electricity consumption recorded from January 2020 to December 2020. The independent variables used for the study include weather, time-dependent factors, and aggregated energy factors. We perform the prediction of consumption using three methods: naive, multiple regression, and extreme gradient boosting regression. The performance of the methods is compared based on the MSE, RMSE, MAE, and MAPE metrics. The study shows that the scores achieved for the extreme gradient boosting method are higher than for the multiple regression. To gain the interpretability of the energy load forecasting and feature analysis the Shapley additive explanations method is used for the best model. As a result, a ranking of the most important factors is created. It shows the importance of aggregated energy factors and weather conditions while placing daylight and season at the bottom of the ranking. © 2022 IEEE.

关键词： Energy utilization

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Multyperson Health Monitoring Wireless Network 30

Multyperson Health Monitoring Wireless Network

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30th Telecommunications Forum, TELFOR 2022

作者： Shevchuk, Bohdan Ivakhiv, Orest Kochan, Roman National Academy of Sciences of Ukraine Glushkov Institute of Cybernetics Department of Numerical Methods Optimization Kyiv03187 Ukraine Lviv Polytechnic National University Computer Technology Automation and Metrology Institute Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine University of Bielsko-Biala Department of Computer Science and Automatics Bielsko-Biala Poland

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

Methodological and algorithmic bases of the local-regional and global wireless networks operation for functional states of people long-term monitoring, processing, encoding, encrypting and transmitting samples of monitoring signals and video data frames using a signaling approach are proposed. The technology is focused on evidence-based monitoring of the condition of operators of various human-machine complexes, athletes and healthy people in order to prevent various diseases. © 2022 IEEE.

关键词： Wireless networks

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis 27

Exploring the Impact of Random Distribution Choices on Parti...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Kolodziejczyk, Joanna Tarasenko, Yuliia Sulikowski, Piotr Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?

arXiv

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arXiv 2023年

作者： Corsini, Andrea Yang, Shanchieh Jay Department of Science and Methods for Engineering University of Modena and Reggio Emilia Modena Italy Department of Computer Engineering Rochester Institute of Technology Rochester United States

关键词： Embeddings

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Leveraging Artificial Intelligence in Online Educational Platforms to Enhance Continuous Professional Development in the Teaching Profession 2

Leveraging Artificial Intelligence in Online Educational Pla...

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2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Amankulovich, Khujakulov Sherzod Tokhirovich, Mannabov Javlon Bakhridinovich, Kadirov Tulkin Kuvandikovich, Taylakov Umid Ananth, Christo Kumar, T. Ananth Samarkand Regional National Center for Training Teachers in New Methods Uzbekistan Uzbekistan Samarkand Agroinnovations and Research University Uzbekistan Samarkand State University Uzbekistan&Adjunct Faculty Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Chennai India Ifet College of Engineering Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350364699

To keep up with the dynamic nature of the modern classroom, teachers must have access to online learning platforms for professional development purposes. This investigation looks at how these platforms can be used to improve teachers' skills, make information sharing easier, and open doors to CPD opportunities. This study examines the relationship between online resources and methods to demonstrate how digital tools have an impact on teacher training and professional development by leveraging AI. It also draws attention to the fact that these platforms are helping with information-methodological support for teachers as well as the ongoing reforms in our nation. Incorporating empirical observations and case studies, the analysis demonstrates how effective online platforms are in fostering a collaborative and innovative environment for professional learning. Digital transformation has the potential to radically alter how teachers obtain professional development, and this study emphasizes that fact. This will enable teachers to effectively address the demands of modern classrooms. © 2024 IEEE.

关键词： Contrastive Learning

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Economic and Mathematical Model of a Group of Gas Fields 14

Economic and Mathematical Model of a Group of Gas Fields

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Solomatin, A.N. Federal Research Center 'Computer Science and Control Russian Academy of Sciences Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (纸本)9781665412308

Economic and mathematical model of a group of gas fields is considered allowing forming the group development strategies for the long-term perspective. An algorithm for calculating the dynamics of gas production by fields and methods for calculating numerous technical and economic indicators of gas production and transport are presented. In addition, information is provided about the software implementation and use of the developed mathematical apparatus. © 2021 IEEE.

关键词： Gas industry

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Economic analysis of one type of criminal activity 14

Economic analysis of one type of criminal activity

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Troyanovskiy, V.M. Federal Research Center 'Computer Science and Control' Russian Academy of Sciences Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (纸本)9781665412308

This paper provides a formal analysis of the regularities of functioning and development of economic crimes. It was expedient to find out the conditions in which theft would not be worthwhile, i.e., supplement legal research with the economic analysis. The analysis results will help to find ways of rational crime counteraction and minimize the damage from it. © 2021 IEEE.

关键词： Economic analysis

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