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International Conference on Advanced Communication technology (ICACT)

作者： Phuc Hao Do Tran Duc Le Vladimir Vishnevsky Aleksandr Berezkin Ruslan Kirichek The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia University of Science and Technology - The University of Danang Da Nang Viet Nam Information Technology Faculty The University of Danang - University of Science and Technology Danang Vietnam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Department of Programming Engineering and Computer Science Bonch Bruevich Saint Petersburg State University of Telecommunications

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.

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Computationally secure threshold secret sharing scheme with minimal redundancy 2

Computationally secure threshold secret sharing scheme with ...

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2nd International Workshop on information, Computation, and Control Systems for Distributed Environments, ICCS-DE 2020

作者： Babenko, M. Tchernykh, A. Golimblevskaia, E. Hung, Nguyen Viet Chaurasiya, V.K. North-Caucasus Federal University Stavropol Russia CICESE Research Center Ensenada BC Mexico South Ural State University Chelyabinsk Russia Ivannikov Institute for System Programming Moscow Russia LeQuyDon Technical University Hanoi Viet Nam Indian Institute of Information Technology Allahabad India

When designing and using distributed storage systems with cloud technology, the security issues become crucial. One of the promising mechanisms is the computationally secure threshold secret sharing scheme. We propose a computationally secure secret sharing scheme based on the minimally redundant modular code. It reduces the computational complexity of data encoding and decoding and reduce data redundancy. We show that it is computationally secure and provides data redundancy equivalent to the redundancy of the Rabin system. We demonstrate that the minimally redundant modular code does not satisfy the criterion of compactness of a sequence, but it can be used as an asymptotically ideal secret sharing scheme. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Redundancy

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How File-access Patterns Inuence the Degree of I/O Interference between Cluster Applications

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2019年第2期6卷 29-55页

作者： Shah, Aamer Kuo, Chih-Song Nomura, Akihiro Matsuoka, Satoshi Wolf, Felix Laboratory for Parallel Programming Technische Universita•t Darmstadt Darmstadt Germany Taiwan Semiconductor Manufacturing Company Limited Hsinchu Taiwan Global Scientic Information and Computing Center Tokyo Institute of Technology Tokyo Japan Department of Mathematical and Computing Science Tokyo Institute of Technology Tokyo Japan

On large-scale clusters, tens to hundreds of applications can simultaneously access a parallel le system, leading to contention and, in its wake, to degraded application performance. In this article, we analyze the inuence of le-access patterns on the degree of interference. As it is by experience most intrusive, we focus our attention on write-write contention. We observe considerable dierences among the interference potentials of several typical write patterns. In particular, we found that if one parallel program writes large output les while another one writes small checkpointing les, then the latter is slowed down when the checkpointing les are small enough and the former is vice versa. Moreover, applications with a few processes writing large output les already can signicantly hinder applications with many processes from checkpointing small les. Such eects can seriously impact the runtime of real applications|up to a factor of ve in one instance. Our insights and measurement techniques oer an opportunity to automatically classify the interference potential between applications and to adjust scheduling decisions accordingly. © The Authors 2019. This paper is published with open access at ***

关键词： Benchmarking

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Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

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SN Computer Science 2020年第2期1卷 1-9页

作者： Hung, Goon Li Sahimi, Mohamad Safwan Bin Samma, Hussein Almohamad, Tarik Adnan Lahasan, Badr Intelligent Biometric Group School of Electrical and Electronic Engineering Engineering Campus Universiti Sains Malaysia Penang Nibong Tebal 14300 Malaysia Department of Computer Programming Faculty of Education–Shabwa University of Aden Shabwa Yemen School of Information Science and Technology Xiamen University Malaysia Jalan Sunsuria Bandar Sunsuria Selangor Darul Ehsan Sepang 43900 Malaysia

Pedestrian detection from a drone-based images has many potential applications such as searching for missing persons, surveillance of illegal immigrants, and monitoring of critical infrastructure. However, it is considered as a very challenge computer vision problem due to the variations in camera point of view, distance from pedestrian, changes in illuminations and weather conditions, variation in the surrounding objects, as well as present of human-like objects. Recently, deep learning-based models are getting more attention, and they have proven a great success in many object detection problems such as the detection of faces, breast masses, and vehicles. As such, this work aims to develop a deep learning-based model that will be applied for the problem of pedestrian detection from a drone-based images. Particularly, faster region-based convolutional neural network (Faster R-CNN) will be used to search for the present of a pedestrian inside the captured drone-based images. To assess the performances, a total of 1500 images were collected by S30W drone and these images were captured at different places, with various views and weather conditions, and at daytime and night-time. Results show that Faster R-CNN was able to achieve a promising result with 98% precision, 99% recall, and 98% F1 measure. Further analysis has been conducted by comparing the outcomes of Faster R-CNN with YOLO deep model on UAV123 publicly available dataset. The reported results indicated that both detection models almost reported very similar results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning Drone images Faster R-CNN Pedestrian detection

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Analysing the impact of COVID-19 outbreak and economic policy uncertainty on stock markets in major affected economies

Analysing the impact of COVID-19 outbreak and economic polic...

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6th Smart Cities Symposium (SCS 2022)

作者： A. Matuka S. S. Asafo G. O. Eweke P. Mishra S. Ray M. Abotaleb T. Makarovskikh A.A. Alhussan A.A. Abdelhamid E.M. El-Kenawy P. Dutta S. Chowdhury University of Macerata Department of Economics and Law Macerata Italy Department of Banking & Finance Niger Delta University Amassoma Nigeria College of Agriculture J.N.K.V.V. Powarkheda India Centurion University of Technology and Management Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog School of Engineering and Technology Amity University Kolkata Kolkata India MCA Department Sri Venkateshwara College of Engineering and Technology Chittoor India

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts.

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Enhancing sleep disorder classification using machine learning and metaheuristic optimization techniques

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Biomedical Signal Processing and Control 2025年 110卷

作者： Amel Ali Alhussan El-Sayed M. El-Kenawy Doaa Sami Khafaga Marwa M. Eid Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Programming School of Information and Communications Technology (ICT) Bahrain Polytechnic PO Box 33349 Isa Town Bahrain Applied Science Research Center. Applied Science Private University Amman Jordan Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 11152 Egypt Jadara Research Center Jadara University Irbid 21110 Jordan

Sleep disorders have a significant impact on physical health, cognitive ability, as well as quality of life. However, they are still underdiagnosed due to the practical limitations of traditional diagnostic methods, such as polysomnography. Polysomnography has been accepted as the gold standard with a good accurate diagnostic value. However, it is laborious, time-consuming and frequently unavailable, particularly in the setting with limited resources. To address these challenges, this study aims to improve sleep disorder classification using machine learning (ML) techniques with metaheuristic optimization strategies. The study utilizes the Sleep Health and Lifestyle Dataset, which includes demographic data, sleep parameters, lifestyle factors, and cardiovascular indicators. To improve model efficiency and predictive accuracy, the binary Al-Biruni Earth Radius (bBER) optimizer is applied for feature selection, effectively reducing data dimensionality by eliminating irrelevant and redundant features. Among the evaluated models, the multilayer perceptron (MLP) achieved the strongest baseline performance, reaching an accuracy of 89.92 % and a sensitivity of 91 % after feature selection. Further optimization through the BER algorithm led to the development of the BER-MLP model, which delivered superior classification results, achieving an accuracy of 95.41 % and a sensitivity of 92.45 %. These findings reinforce the effectiveness of integrating metaheuristic optimization with machine learning approaches to enhance diagnostic accuracy and reliability in sleep disorder detection.

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Statistical optical image analysis for information system 7

Statistical optical image analysis for information system

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7th International Conference on Future Internet of Things and Cloud Workshops, FiCloudW 2019

作者： Ryazantsev, Oleksandr Khoroshun, Ganna Riazantsev, Andrii Ivanov, Vitalii Baturin, Aleksandr Volodymyr Dahl East Ukrainian National University Faculty of Information Technology and Electronics Severodonetsk Ukraine Dept. of Programming and Mathematics Volodymyr Dahl East Ukrainian National University Severodonetsk Ukraine

ISBN: (纸本)9781728144115

Optical image processing methods are important for creation and development of an automated measuring information optical system. Statistical methods provide additional information, a better understanding of the object that is being studied. They allow describing main features of the image by several parameters, such as mean value, median and standard deviation. For correlation of the computer simulation and experimental data the method of signal quantization is described. It is defined the parameters of the fitted normal distribution for the optical image variation curve and consent criteria of empirical and theoretical data by Romanovsky method. Obtained results can be used as the preliminary analysis for digital processing of the optical image with application in compression and encryption, holography, identification and tracking. © 2019 IEEE.

关键词： Optical systems

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Synthesis Algorithm for a Continuous Robust Regulator on a Manifold for a Manipulator under Non-Random Noise Conditions

Synthesis Algorithm for a Continuous Robust Regulator on a M...

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International Russian Automation Conference (RusAutoCon)

作者： A. A. Kolesnikov S. I. Kolesnikova Institute of Computer Technology and Information Security South Federal University Taganrog Russia Institute of Computational Systems and Programming St.Petersburg State University of Aerospaсe Instrumentation St. Petersburg Russia

ISBN: (数字)9781728161303

ISBN: (纸本)9781728161310

The nonlinear adaptation algorithm is considered as applied to stabilization of the center of mass of a moving object subject to nonrandom noise in the control variable. An unknown disturbance is a bounded and continuous function of time. To solve the problem of its modeling and suppression, the following result is used: continuous functions can be described as partial solutions of some differential equations. The developed control algorithm guarantees full compensation of the external disturbance under certain conditions. The simulation result of the obtained control is applied to a manipulation object.

关键词： Mathematical model Regulators Manifolds Automation Aerospace electronics Gold

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Abnormal Traffic Detection Method Based on DCNN-GRU Architecture in SDN

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International Journal of Intelligent Systems 2025年第1期2025卷

作者： Xueyuan Duan Kun Wang Yu Fu Taotao Liu Yihan Yu Jianqiao Xu Lu Wang College of Computer and Information Technology Xinyang Normal University Xinyang 464000 Henan China *** Department of Information Security Naval University of Engineering Wuhan 430033 Hubei China *** Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang 464000 China *** School of Information and Communication Engineering Xinyang Vocational and Technical College Xinyang 464000 Henan China *** Department of Operational Research and Programming Naval University of Engineering Wuhan 430033 China *** Department of Economic Crime Investigation Henan Police College Zhengzhou 450046 Henan China

In response to the centralized single-architecture abnormal traffic detection method in Software Defined Network (SDN), which consumes massive computational and network resources, and may lead to the decline of service quality of SDN network, this paper proposes a large-scale abnormal traffic detection method of SDN network based on Distributed Convolutional Neural Networks and Gate Recurrent Unit (DCNN-GRU) architecture. This method utilizes lightweight detection agents based on CNN deployed on each controller to extract traffic features preliminarily. Then it inputs the feature data into the GRU-based deep detection model hosted in the cloud for collaborative training and completes the final abnormal detection task. Since the feature extraction tasks are distributed across multiple controllers, the cloud server only needs to relearn and classify the extracted feature data, which is less costly than directly extracting feature information from the original traffic data and occupies less bandwidth resources than transmitting complete data packets. The experiment shows that the method achieves an abnormal detection accuracy of 0.9939, a recall rate of 0.9831, and a false alarm rate of only 0.0244, obtaining a higher precision and lower false alarm rate than traditional detection methods.

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2021年 1221 AISC卷 v-vi页

作者： Balas, Valentina Emilia Jain, Lakhmi C. Balas, Marius Mircea Shahbazova, Shahnaz N. "Aurel Vlaicu" University of Arad Faculty of Engineering Arad Romania Faculty of Engineering and Information Technology University of Technology Sydney Centre for Artificial Intelligence Sydney Australia Faculty of Engineering "Aurel Vlaicu" University of Arad Arad Arad Romania Department of Information Technology and Programming Azerbaijan Technical University Baku Azerbaijan

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