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Developing Robust Cybersecurity Policies and Governance Frameworks in Response to Evolving Legal and Regulatory Landscapes

Developing Robust Cybersecurity Policies and Governance Fram...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Pandey, Radhika Anjimoon, Shaik Asha, V. Singla, Atul Khan, Irfan Abed, Zain Alabdeen Hasan GyanGanga Institute of Technology and Science DoCSE M.P Jabalpur India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This research introduces a comprehensive cybersecurity framework consisting of five interlinked algorithms: Threat Intelligence Integration, Risk Assessment and Management, Compliance Mapping, Incident Response Planning, and Employee Training and Awareness. Through comparative performance evaluation and ablation study, the framework demonstrates consistent superiority across key parameters. Visual representations, including bar charts, line charts, and pie charts, provide intuitive insights into effectiveness, adaptability, compliance, and overall performance. The ablation study underscores the synergistic importance of each algorithm, emphasizing the framework's holistic nature. This research contributes to advancing cybersecurity methodologies, offering a dynamic and adaptive approach to safeguarding against evolving cyber threats. © 2024 IEEE.

关键词： Cybersecurity

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Fostering Sustainable Development through Distributed Intelligence Applications

Fostering Sustainable Development through Distributed Intell...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Durga, C.S.L. Vijaya Agrawal, Shruti Anandhi, R.J. Singh, Navdeep Kalra, Ravi Adnan, Myasar Mundher Institute of Aeronautical Engineering Hyderabad Dundigal India Ies College of Technology Department of Computer Science & Engineering Ma. P Bhopal India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India The Islamic University Najaf Iraq

ISBN: (纸本)9798350373783

SDODI, which stands for Sustainable Development Optimization using Distributed Intelligence, advances sustainable development. SDODI is the first to examine environmental, social, and economic challenges together using EcoOpt, InclusNet, and ResilienceHub. Environmental optimization program EcoOpt increases the total effect while ensuring resource efficiency and environmentally friendly behavior. InclusNet uses math to include everyone and make communities more active, varied, and accessible. The ResilienceHub algorithm prioritizes financial stability, flexibility, and economic power. Sustainable initiatives are typically divided by traditional approaches. SDODI utilizes information to better allocate resources, engage the community, and strengthen the economy. SDODI is unique because it adapts to changing situations, giving it a comprehensive sustainable growth solution. SDODI is new because it supports and improves sustainability ideals. © 2024 IEEE.

关键词： Sustainable development goals

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On the security of authenticated key agreement schemes for e-healthcare

On the security of authenticated key agreement schemes for e...

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2022 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2022

作者： Kasi, Abhijit Obaidat, Mohammad S. Rewal, Purva Mishra, Dheerendra Hsiao, Kuei-Fang Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics and Computer Applications Bhopal462003 India Computer Science Department Cybersecurity Center University of Texas-Permian Basin OdessaTX79762 United States University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China University of Texas-Permian Basin Computer Science Department OdessaTX79762 United States

ISBN: (数字)9781665486170

ISBN: (纸本)9781665486170

With the rapid development of Internet and communication technology and the advances in devices' computing power, people can avail the services of e-healthcare with ease. To guarantee the authorized access of data in e-healthcare services, numerous authentication schemes have been proposed. However, the analysis of existing protocols along with efficiency is required before their adoption. To understand the key security challenges in existing design and relation to efficiency, we present a case study on recent authentication schemes for e-healthcare. For our study, we considered Chen and Chen (2021) authentication scheme for e-health, Dharminder et al. (2021) chaotic map-based authentication scheme, and Wu et al. (2021) authentication scheme for e-healthcare. We perform cryptanalysis to demonstrate the security vulnerabilities and to understand the key factor behind the security failure. We also analyze the performance of recent designs and present a discussion on their limitations and advantages. We also demonstrate comparative analysis in terms of execution cost and security attributes to find their execution efficiency in response to security. © 2022 IEEE.

关键词： Efficiency

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Algorithmic Intelligence Instantaneously for Complex Real-Time Supply Chain Optimization

Algorithmic Intelligence Instantaneously for Complex Real-Ti...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Lalitha, G. Rajora, Himanshu Kumar Asha, V. Nijhawan, Ginni Praveen Al-Shami, Karrar R. Institute of Aeronautical Engineering Hyderabad Dundigal India GyanGanga Institute of Technology and Sciences Docse M.P Jabalpur India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Science Department of Forensic Sciences Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

The modern world of supply chain management is complex and ever-evolving, thus the ability to make informed decisions and react quickly are of the utmost importance. In response to these issues, we have created a ground-breaking method that we call 'Dynamic Real-Time Supply Chain Optimization with Algorithmic Intelligence (DRT-SCAI).' Better real-time visibility, autonomous decision-making, predictive accuracy, efficiency, customer happiness, risk mitigation, and sustainability are all outcomes of this suggested methodology's use of state-of-the-art algorithmic intelligence, which simultaneously optimizes the supply chain in an instant. By using this approach, several enhancements are realized. To achieve these improvements, it is necessary to use this strategy. © 2024 IEEE.

关键词： Customer satisfaction

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Compact and Monotone Difference Schemes for Parabolic Equations

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Mathematical Models and computer Simulations 2021年第6期13卷 1038-1048页

作者： Matus, P.P. Utebaev, B.D. Institute of Mathematics National Academy of Sciences of Belarus Minsk Belarus Institute of Mathematics and Computer Science the John Paul II Catholic University of Lublin Lublin Poland

Abstract: In this paper, we consider compact and monotone difference schemes of the fourth order of approximation for linear, semilinear, and quasi-linear equations of the parabolic type. For the Fisher equation, the monotonicity, stability, and convergence of the proposed methods in the uniform norm L_∞ or C are proved. The results obtained are generalized to quasi-linear parabolic equations with nonlinearities of the porous medium type. In this study, at an abstract level, a definition of the monotonicity of a difference scheme in the nonlinear case is given. The performed computational experiment illustrates the efficiency of the methods under consideration. This article indicates a method for determining the order of the rate of convergence of the proposed methods based on the Runge method in the case of the presence of several variables and different orders in different variables. © 2021, Pleiades Publishing, Ltd.

关键词： compact difference schemes maximum principle monotone difference schemes two-sided estimates

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Guest Editorial to the Special Issue Public Transport Optimization: From Theory to Practice

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Operations Research Forum 2024年第3期5卷 73页

作者： Cacchiani, Valentina Müller-Hannemann, Matthias Rojas-Marcos, Federico Perea Department of Electrical Electronic and Information Engineering “Guglielmo Marconi” University of Bologna Viale Risorgimento 2 Bologna 40136 Italy Institute for Computer Science Martin Luther University Halle-Wittenberg Von-Seckendorff-Platz 1 Halle D 06120 Germany Department of Applied Mathematics II and Institute of Mathematics (IMUS) University of Seville Avda. Reina Mercedes s/n Seville 41012 Spain

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Next-Generation Artificial Intelligence Solutions for Comprehensive Analysis and Improvement of Heat Dissipation in High-Intensity Boiling Environments

Next-Generation Artificial Intelligence Solutions for Compre...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Gadiyar, H. Manoj T. Kumar, S Vinod Raj, Vijilius Helena Yadav, Dinesh Kumar Hasoni, Mortada Kareem Sabi Lakhanpal, Sorabh Canara Engineering College Department of Computer Science and Business System India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology AlTalb Collage of Nursing Dhi Qar64001 Iraq Lovely Professional University Phagwara India

ISBN: (纸本)9798350373783

This research delves into the realm of AI-driven heat dissipation, offering a suite of algorithms to address the challenges posed by high-intensity boiling environments. The proposed predictive modeling algorithm, rooted in linear regression, provides a systematic and robust approach through 20 meticulously designed steps. This algorithm, along with neural networks, reinforcement learning, generative adversarial networks (GANs), and genetic algorithms, forms a versatile toolbox for thermal management. Linear regression, through its step-by-step predictive modeling process, ensures accurate heat dissipation predictions. Neural networks, employing backpropagation, showcase adaptability and complexity, excelling in real-time adaptation. Reinforcement learning, specifically Q-Learning, introduces dynamic decision-making strategies, vital for navigating complex thermal scenarios. GANs contribute by generating realistic synthetic data, an invaluable asset for simulating high intensity boiling environments. Genetic algorithms, resembling natural selection, prove versatile and scalable in evolving optimal solutions. The research evaluates these algorithms comprehensively, demonstrating the proposed method's superiority in prediction accuracy, energy efficiency, adaptability, and overall performance. The proposed predictive modeling algorithm stands out as a benchmark, showcasing its potential for practical implementation and optimization in diverse scenarios. This comprehensive exploration sets the stage for innovative solutions to the intricate challenges of thermal management, as continuous adaptation and fine-tuning remain pivotal in this dynamic landscape. © 2024 IEEE.

关键词： Generative adversarial networks

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Adaptive Web Crawling Strategies Based on Ontological User Interest Modeling for Personalized Content Retrieval 2

Adaptive Web Crawling Strategies Based on Ontological User I...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Durga, Vijaya Anandhi, R.J. Bansal, Saloni Singh, Navdeep Kalra, Ravi Bader, Nabaa M. Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park IIUttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

The exponential growth of web content necessitates efficient and personalized methods for information retrieval. This research introduces an innovative approach to web crawling, centered on adaptive strategies guided by ontological user interest modeling, aiming to enhance the precision of personalized content retrieval. The proposed method employs a dynamic user interest model, constructed, and continually refined through user interactions, leveraging ontology to represent and understand the complex and evolving nature of user interests. In contrast to traditional web crawlers that operate on fixed algorithms, the adaptive crawler anticipates changes in user interests and adjusts its strategies, accordingly, ensuring relevance and timeliness of the retrieved content. The system architecture integrates a multi-faceted profiling mechanism that captures and evolves with user preferences, combined with an intelligent crawling algorithm that prioritizes content based on predicted user interest levels. Empirical results demonstrate a significant improvement in retrieval precision and efficiency, highlighting the crawler's ability to discern and adapt to the nuanced shifts in user interests. Furthermore, the research delves into the implications of such personalized crawling on user privacy and web dynamics, offering insights and recommendations for a balanced approach. This work contributes to the fields of semantic web, personalized content retrieval, and adaptive systems, proposing a novel pathway for the next generation of intelligent web crawlers. © 2024 IEEE.

关键词： Web crawler

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First Competition on Presentation Attack Detection on ID Card

First Competition on Presentation Attack Detection on ID Car...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Juan E. Tapia Naser Damer Christoph Busch Juan M. Espin Javier Barrachina Alvaro S. Rocamora Krištof Ocvirk Leon Alessio Borut Batagelj Sushrut Patwardhan Raghavendra Ramachandra Raghavendra Mudgalgundurao Kiran Raja Daniel Schulz Carlos Aravena Hochschule Darmstadt (h_da) da/sec-Biometrics and Internet Security Research Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Facephi Company Spain Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Norwegian University of Science and Technology (NTNU) Gjøvik Norway ID VisionCenter (IDVC) Santiago Chile

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of ten registered teams, both from academia and industry. In the end, the participating teams submitted five valid submissions, with eight models to be evaluated by the organisers. The competition presented an independent assessment of current state-of-the-art algorithms. Today, no independent evaluation on cross-dataset is available; therefore, this work determined the state-of-the-art on ID cards. To reach this goal, a sequestered test set and baseline algorithms were used to evaluate and compare all the proposals. The sequestered test dataset contains ID cards from four different countries. In summary, a team that chose to be "Anonymous" reached the best average ranking results of 74.80%, followed very closely by the "IDVC" team with 77.65%.

关键词： Biometrics Training Metalearning Industries Accuracy Biological system modeling Benchmark testing Proposals Few shot learning Stress

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