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Technique for Evaluating the Effectiveness of the information security Department 16

Technique for Evaluating the Effectiveness of the Informatio...

引用

16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Belov, Viktor M. Pestunova, Tamara M. Belova, Nadezhda V. Kosov, Dmitry L. Information Security Dept. Novosibirsk State Technical University Novosibirsk Russia Computer Systems Dept. Novosibirsk State University Novosibirsk Russia Economics and Planning Dept. PJSC Rosneft Oil Company Moscow Russia

ISBN: (纸本)9798350330885

This article proposes a technique that establishes the procedure for evaluating the level of efficiency of the information security department (an employee performing information security functions). The technique uses performance evaluation criteria based on the apparatus of fuzzy logic, the composition of fuzzy relations. The technique describes the procedure for evaluating the effectiveness of the information security department (information security officer) during audits in the area of "Organization and state of work on information protection", self-assessment of the effectiveness of work. The method of assessing the level of efficiency consists in presenting with the help of a set of measurements (both at the quantitative and qualitative level) the features collected to build a classification of the effectiveness of the information security department (information security officer). Based on a set of measurements of signs, the decision-maker must determine (classify) the effectiveness of work using the criteria for assessing the quality of their work. In the future, the methodology can be expanded for additional purposes of predicting the level of security of informatization objects. © 2023 IEEE.

关键词： Classification (of information)

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CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Forecasting

CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Fo...

引用

2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Farid, Karim S. Ali, A.A. Salem, Sameh A. Mohamed, Amr E. Egyptian Electricity Holding Company Dept. Information Systems & Communications Cairo Egypt Helwan University - Faculty of Engineering Dept. Electrical Power Cairo Egypt Helwan University - Faculty of Engineering Dept. Computer & Systems Engineering Cairo Egypt

ISBN: (纸本)9798350326062

With the development of plans to replace conventional meters with smart meters to enhance the functionality and performance of power system, forecasting power consumption is still a challenge due to nonlinearity patterns, different types of noise in the datasets, and sometimes lack of data. Artificial Intelligence plays an important role in utilizing the data sent from smart meters, particularly in the area of short-term load forecasting, which is required to meet the needs of consumers. Based on such insight, this study suggests a hybrid model based on a one-dimension convolutional neural network and gated recurrent units (CONV1D-GRU), to forecast hourly power consumption. The outcome demonstrates that the suggested model is capable of encompassing the patterns in a historical data pertaining the hourly power use. The suggested model has outperformed the competing strategies in the mean absolute percentage error (MAPE%), and the coefficient of determination (R^2) metrics. © 2023 IEEE.

关键词： Smart meters

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TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forecasting 1

TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forec...

引用

1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

ISBN: (纸本)9798350335569

Using artificial intelligence (AI) to anticipate electrical consumption is one of the used applications of this technology. The analysis and training of AI models are done using data related to electricity consumption, including consumption itself, temperatures, working hours, the number of inhabitants, etc. AI models can help to reduce electricity expenses and consumption, which helps in identifying peak use periods and creating more efficient energy plans. Another use of artificial intelligence is to advise users on how to use less energy in a more efficient manner. Based on such insight, this study suggests a hybrid model based on a temporal convolutional network and multilayer perceptron (TCN-MLP), to forecast hourly power consumption. The results show that the recommended model can account for the patterns in the historical data relating to hourly power usage. TCN-MLP hybrid model has proved to be better than competing approaches in the coefficient of determination (R2) and the mean absolute percentage error (MAPE%) metrics. © 2023 IEEE.

关键词： Electric power utilization

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Enhancing Performance in Hybrid Satellite-Terrestrial networks: A Novel Joint NOMA-NC Approach

Enhancing Performance in Hybrid Satellite-Terrestrial Networ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Karavolos, Michail Moraitis, Nektarios Tsipi, Lefteris Vouyioukas, Demosthenes University of the Aegean Dept. of Information and Communication Systems Engineering Samos83200 Greece National Technical University of Athens School of Electrical and Computer Engineering Zografou15773 Greece University of Piraeus Dept. of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350351255

An appealing alternative towards dealing with mobility and coverage challenges in future wireless networks, are hybrid satellite-terrestrial networks (HSTNs). The combination of network coding (NC) and non-orthogonal multiple access (NOMA) techniques, have recently gained popularity due to their performance gains in improving the quality of a wireless transmission and exploiting the accessible spectrum. This paper introduces a joint NOMA-NC approach, integrated to an HSTN comprising a satellite, a terrestrial base station (BS), and multiple ground-based mobile terminals (MTs). The suggested method facilitates a cluster of end-users to be concurrently served through NOMA from a satellite using optimal power allocation, whereas the BS utilizes random linear NC to augment the MTs' reception in the event of errors. The simulation results confirm the effectiveness of the proposed methodology in improving system sum throughput, demonstrating substantial enhancements compared with both OMA with NC, and standalone NOMA and OMA approaches. The resilience of the suggested method is proved, improving the sum throughput even at low power transmissions and in the worst case channel conditions. © 2024 IEEE.

关键词： network coding

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Energy Efficient joint user association and power allocation using Parameterized Deep DQN 9

Energy Efficient joint user association and power allocation...

引用

9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Yap, Kian Meng Habaebi, Mohamed Hadi Islam, Md. Rafiqul Sunway University Dept. of Computing and Information Systems Petaling Jaya Malaysia University of Turku Dept. of Computing Turku Finland Cardiff Metropolitan University Dept. of Electronics and Computer Systems Engineering Cardiff United Kingdom International Islamic University Dept. of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350325218

Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems. © 2023 IEEE.

关键词： Energy efficiency

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Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping 2

Enhancing Credit Card Fraud Detection through LSTM-Based Seq...

引用

2nd IEEE International Conference on networking and Communications, ICNWC 2024

作者： Jhansi Ida, S. Balasubadra, K. Skandarsini, R.R. Lakshmi Narayanaa, T. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India RMD Engineering College Department of Information Technology Thiruvallur India

ISBN: (纸本)9798350365269

There is still a serious problem with credit card fraud in the world of financial transactions, requiring innovative solutions to effectively detect and halt illicit behavior. This research presents a unique method of detecting credit card fraud by strategically implementing early pausing in a Long Short-Term Memory (LSTM) neural network. Leveraging the sequential dependencies inherent in transaction data, the LSTM model demonstrates remarkable efficacy in discerning fraudulent from legitimate transactions. The early stopping mechanism ensures that the model's training halts at the optimal epoch, preventing over fitting and enhancing generalization. Our thorough analysis highlights the model's versatility by demonstrating a dynamic compromise between recall and accuracy throughout training. Practical implications include enhanced fraud detection accuracy, reduced false alarms, and real-time decision support for financial institutions. The model's adaptability and generalization make it a valuable asset in the ongoing battle against credit card fraud, contributing to a more secure financial environment. Future research directions may explore additional features and alternative architectures to further advance fraud detection capabilities. © 2024 IEEE.

关键词： Long short-term memory

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security in remote access, based on Zero Trust Model concepts and SSH authentication with signed certificates 11

Security in remote access, based on Zero Trust Model concept...

引用

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI 2022

作者： Iță, Cristina-Raluca Constantinescu, Rodica-Claudia Vlădescu, Alexandru Alexandrescu, Bogdan Information and Computer Systems Engineering POLITEHNICA University of Bucharest Romania Applied Electronics and Information Engineering Dept. POLITEHNICA University of Bucharest Romania

ISBN: (纸本)9781510660939

This paper aims to resume the information on the various ways in which security can be achieved when it comes to accessing external resources from the outside through company devices or personal devices. When planning security policies, it is essential to start with the idea that anything outside is exposed to the risk of infection. We will analyze the Zero Trust model and other security concepts in order to use them for remote access. The model has been designed to adapt to the modern needs of the workforce and employers, including remote work in terms of maximum cyber security, minimizing the risk of resource exploitation (people, devices, applications or data). In order to make the transition to such a model, this paper will implement and test a solution for remote management of resources (applications, servers), which implements some of the principles of Zero Trust architectures, starting from the classic version SSH (Secure Shell) access. The solution involves accessing, securing and authorizing a secure virtual machine via SSH, and then securing access to production resources. The technical mechanism by which these operations are performed presupposes that after authentication on the buffer machine a user certificate is automatically requested which will be signed by a certification authority. This certificate contains the group to which the operator belongs so that, by presenting this certificate, it is allowed to authenticate, authorize and audit access to the resources that will be managed by the user in that session. © 2023 SPIE.

关键词： Authentication

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Neural network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification

Neural Network-Based Analysis of Forest Fire Aftermath in Cl...

引用

2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing

作者： Hnatushenko, Viktoriia Hnatushenko, Volodymyr Soldatenko, Dmytro Dept. Information Technologies and Systems Ukrainian State University of Science and Technologies Dnipro Ukraine Dept. Information Technologies and Computer Engineering Dnipro University of Technology Dnipro Ukraine Institute of Photogrammetry and GeoInformation Leibniz Universität Hannover Germany

Today's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalanced sample distribution is a critical problem in this regard. Traditional machine learning algorithms in particular have limitations in dealing with sample imbalance. In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. To illustrate our method, we use Sentinel-2 remote sensing (RS) images covering a number of regions in Ukraine, and then we create an image dataset of the region and for training and testing make data augmentation. The models with different architectural features were investigated. The results demonstrate that the proposed CNN has a higher classification accuracy than the ones discussed in the paper: the classification accuracy on the test dataset reached 96.7% with intersection-over-union values of up to 89.7%. This opens the way for further research in the direction of refining algorithms for classify satellite data with an imbalanced class structure. © Author(s) 2024.

关键词： Semantic Segmentation

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Exploring Post-Quantum Cryptographic Algorithms for Secure Data Transmission 3

Exploring Post-Quantum Cryptographic Algorithms for Secure D...

引用

3rd IEEE International Conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Gulomov, Sherzod R. Khudayberganov, Temur R. Ravshanova, Maftuna X. Turdiev, Temur T. Atabayev, Sultonbek S. Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dean of the Faculty of Cyber Security Urgench Uzbekistan Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Information security Urgench Uzbekistan Urgench Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Computer engineering Urgench Uzbekistan Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Information technology Urgench Uzbekistan Urgench Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Computer engineering Urgench Uzbekistan

ISBN: (纸本)9798331516321

The rapid advancement of quantum computing introduces a critical threat to established cryptographic systems, such as RSA and ECC, by enabling quantum attacks those classical systems cannot withstand. This evolving threat creates a necessity for post-quantum cryptographic (PQC) algorithms to ensure secure data transmission in the quantum era. PQC encompasses diverse approaches, including lattice-based, hash-based, code-based, and multivariate polynomial cryptography. Each category offers distinct advantages and challenges: lattice-based algorithms provide robust security but may require substantial computational resources, while hash-based approaches are easier to implement but limited in flexibility. Code-based algorithms deliver strong security with significant computational demand, and multivariate polynomial approaches boast efficiency, though they face scrutiny over quantum resilience. This paper evaluates these PQC algorithms in terms of their security, performance, and feasibility, identifying optimal methods to secure data transmission in a future where quantum threats are prevalent. © 2024 IEEE.

关键词： Quantum cryptography

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AirWave: Enhancing UAV Connectivity in Cellular networks through an Integrated Simulation Framework

AirWave: Enhancing UAV Connectivity in Cellular Networks thr...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Zor, Emirhan Taskiran, Yusuf Dael, Fares A. Shayea, Ibraheem Rzayeva, Leila Syzdykova, Zuleikha Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey İzmir Bakirçay University Management Information Systems İzmir Turkey Astana IT University Dept. of Intelligent Systems and Cybersecurity Astana Kazakhstan Astana IT University Dept. of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350374100

This study presents AirWave, an innovative modeling framework tailored to address the challenges associated with integrating Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks. With the rising prominence of UAVs across various industries, ensuring uninterrupted connectivity and efficient mobility management within cellular networks becomes paramount. AirWave offers a comprehensive simulation environment, amalgamating network and physics simulations using ns-3 for network simulation, Gazebo for physics simulation, and integrating PX4 autopilot for realistic UAV flight dynamics. This framework facilitates in-dept. investigations into UAV mobility and handover management, including a trajectory optimization technique based on reinforcement learning for cellular-connected drones. Extensive simulations demonstrate the versatility and effectiveness of AirWave, showcasing notable improvements in UAV range, connectivity, and management within cellular networks. These findings hold significant promise for advancing research and practical applications in UAV communications. © 2024 IEEE.

关键词： Reinforcement learning

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