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Enhancing the Security of Public Key Cryptography in Addressing Quantum computing Threats

Enhancing the Security of Public Key Cryptography in Address...

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2024 international conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Oguntoyinbo, Oluwole Ofori-Yeboah, Abel Ikenga-Metuh, Chukwuebuka Ghimire, Yogesh Obode, Efua Brissett, Adrian School of Computing and Eng University of West London London United Kingdom

ISBN: (纸本)9798331539733

The potential of quantum computing to compromise traditional public-key cryptography (PKC) has led to a pressing need for more efficient and resilient cryptographic methods. Recent advancements, such as the 800x improvement in the logical error rate by Microsoft and QUANTINUUM, underscore the urgency of this issue. Post-quantum cryptography (PQC) offers a promising solution designed to withstand quantum attacks. This paper examines how PQC and other advanced cryptographic techniques can enhance the security and efficiency of PKC in the quantum era. current PKC methods, despite revolutionizing secure online communication, are vulnerable to flaws in their underlying algorithms. The proposed hybrid model integrates PQC with traditional PKC, leveraging both technologies' strengths to mitigate quantum threats while preserving established security benefits. The investigation addresses critical research questions: how to enhance PKC key management for better efficiency and security, and which PQC techniques can protect PKC from quantum attacks. This paper's contribution includes identifying vulnerabilities in existing PKC, comparing RSA and post-quantum algorithms, and implementing a PQC algorithm between a client and server to analyze integration with existing protocols. Finally, the paper recommends security mechanisms to improve PQC protocols, ensuring robust protection against emerging quantum computing capabilities. © 2024 ieee.

关键词： Quantum computers

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Analysis on Recent trends in Augmented and Virtual Reality with Haptic Interferences 11

Analysis on Recent Trends in Augmented and Virtual Reality w...

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11th ieee international conference on Cloud computing in Emerging Markets, CCEM 2023

作者： Potluri, Swathi Kumar, N.K. Senthil Department of Computer Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350360059

Tele-haptics, telemedicine, including training, education, and amusement are a few examples of applications for the sense of touch (haptics). A wide range of multi- and cross-disciplinary sectors are now using and researching haptics. The inclusion of new media kinds, such as music, film, and even aroma, expands the usage of haptics. In order to provide users a more realistic experience, haptics, for instance, are widely employed in virtual reality situations. While haptic interface science has improved throughout the years, that are still a number of barriers that stand in the way of further development. In this overview, the tactile interfaces for virtual reality—which comprise wearables, handhelds, encountered-type devices, props, and mid-air methods—are discussed. This study intends to investigate the current state and potential uses of hands-on material-based haptic technology, with a focus on VR/AR. ©2023 ieee.

关键词： Virtual environments

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Work-in-Progress: Security of Public-Key Schemes in the Quantum computing Era - A Literature Review

Work-in-Progress: Security of Public-Key Schemes in the Quan...

引用

2023 ieee international Black Sea conference on Communications and Networking, BlackSeaCom 2023

作者： Redkins, Boriss Kuzminykh, Ievgeniia Ghita, Bogdan King's College London Department of Informatics London United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom

ISBN: (纸本)9798350337822

Quantum computers, although still in the theoretical stage, conceptually have the power to break many current cryptosystems, including public-key schemes., for example, RSA (Rivest, Shamir., Adleman), elliptic-curve cryptography (ECC), and ElGamal. NIST specialists and national governments believe that existing cryptography algorithms will become vulnerable to brute force analysis by the end of the decade and agree that measures should be taken before then to ensure the secrecy of current and future algorithms. In this context, information security, including confidentiality and integrity, must be considered. This paper provides a systematic literature review on whether existing public-key schemes have the necessary complexity and solution space to resist a quantum computer. The discussion will focus on a quantum computer's negative 'real-world' impact and examine existing post-quantum algorithms, then provide analysis across two selected dimensions. Finally, we summarise the latest trends and gaps in post-quantum cryptography and provide the security analysis of NIST's last round finalists. © 2023 ieee.

关键词： Public key cryptography

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Resource Allocation in Edge computing Environment Using Deterministic Policy Gradient Algorithm 10

Resource Allocation in Edge Computing Environment Using Dete...

引用

10th international conference on advanced computing and Communication Systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 ieee.

关键词： Mobile edge computing

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FlarePath: advanced Vehicle Fire Safety and Monitoring with Rapid Emergency Dispatch Solutions 6

FlarePath: Advanced Vehicle Fire Safety and Monitoring with ...

引用

6th international conference on Advancements in computing, ICAC 2024

作者： Anthick, Glen Dharmagunawardana, Pasindu Abeywardhana, Dewmini Peramunage, Aveen Amarasena, Nelum Chathuranga Siriwardana, Deemantha SLIIT Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

Vehicle fires pose serious risks, often resulting in severe damage and potential fatalities. current detection systems are primarily reactive, failing to predict and mitigate fire hazards in advance. This research proposes an integrated solution that combines IoT, machine learning, and cloud computing to enhance vehicle fire safety and emergency response. The system uses IoT-enabled sensors to monitor fire indicators like smoke, temperature, and vibrations. Machine learning algorithms analyze this data for early fire prediction, while real-time fire information and vehicle location are transmitted to the nearest fire department, optimizing emergency response with real-time and historical traffic data. An IoT-based automatic fire extinguisher mechanism is implemented to reduce fire impact immediately upon detection. The methodology involves installing sensors, processing data with machine learning, and relaying it to the cloud for analysis. Alerts inform drivers and owners about fire incidents and nearby extinguishers, improving response times. The proposed solution significantly enhances fire safety, demonstrating high accuracy in fire prediction and severity classification. © 2024 ieee.

关键词： Fire extinguishers

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The Research on Cloud Monitoring System Architecture of Flight Test 5

The Research on Cloud Monitoring System Architecture of Flig...

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5th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Yang, Zhe Chinese Flight Test Establishment Shaanxi Province Xi'an China Northwestern Polytechnical University Shaanxi Province Xi'an China

ISBN: (纸本)9798350357950

To meet the needs for real-time monitoring of future test flights, this article introduces the current monitoring system status and development trends, and proposes a flight test cloud monitoring system architecture, and elaborates the key technologies for implementing flight test cloud monitoring systems in detail. The system-related technology should be used for reference in the test flight. © 2023 ieee.

关键词： Data distribution service Flight test Kloud computing Web monitoring

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Data Management in IoT: From Sensor Data to Intelligent Applications

Data Management in IoT: From Sensor Data to Intelligent Appl...

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ieee lnternational conference on advanced trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2022

作者： Vasyutynskyy, Volodymyr Luntovskyy, Andriy Hans-Grundig-Str. 25 Dresden01307 Germany

ISBN: (纸本)9783031249624

Emerging networks allow to connect the smart devices and applications efficiently and everywhere, opening a lot of opportunities and use cases for getting and using data. The resulting huge amount of heterogeneous data leads to the new challenges on the data management, like data storage, processing, privacy, lifecycle etc. This work gives and overview of these challenges and the current solutions like cloud computing and data mesh. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

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Cognitive computing for Marketing Decision Support 2

Cognitive Computing for Marketing Decision Support

引用

2nd international conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024

作者： Harshitha, G.M. Madanan, Pawan Agrawal, Shital Muralidhar, L.B. Natarajan, Sundarapandiyan Pechetty, Venkata Purnakumari Department of Mca Udupi India Trine University College of Graduate and Professional Studies AngolaIN46703 United States Shivajirao S. Jondhle College of Engineering & Technology Asangaon India School of Commerce Department of Bms Bangalore India Adithya Institute of Technology Department of Management Studies Kovilpalayam Coimbatore India Sr University School of Busniess Telangana Warangal India

ISBN: (纸本)9798350364699

The use of artificial intelligence (AI) in cognitive computing allows for the simulation of human learning processes and the improvement of decision-making across a comprehensive range of fields. Within the context of marketing decision support, this study investigates the application of cognitive computing and focuses on how advanced artificial intelligence technologies might transform marketing tactics. Cognitive computing systems provide practical perceptivity that may be used to inform strategic opinions, maximize campaign performance, and exemplify client relations. This is accomplished by analyzing enormous datasets and observing patterns in consumer gestures. For this article, case studies that demonstrate the efficacy of cognitive computing in predicting request trends, segmenting target demographics, and perfecting client engagement are reviewed. Additionally, it addresses the issues that are associated with implementing cognitive computing in marketing, such as the integration of data, the transparency of algorithms, and ethical considerations being taken into account. According to the findings, cognitive computing presents a great opportunity for improving marketing decision-making processes, which in turn facilitates the development of strategies that are both better informed and more effective. The investigation of new directions for exploration includes the investigation of developments in cognitive computing technologies and the integration of these technologies with emerging marketing tools and platforms. © 2024 ieee.

关键词： Marketing

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Predictive Analytics for Early Electrical Fire Detection in IoT Networks Using Random Forest 2

Predictive Analytics for Early Electrical Fire Detection in ...

引用

2nd ieee international conference on Contemporary computing and Communications, InC4 2024

作者： Raman, Ramakrishnan Rajesh, Shivani Symbiosis Institute of Business Management Pune & Symbiosis International Deemed University Maharashtra Pune India Maharashtra Pune India

ISBN: (纸本)9798350383652

Ensuring safety against potential fire threats is of utmost significance due to the development of IoT devices in numerous areas. Most of the time, human involvement is required for traditional fire detection systems, which lack proactive capabilities. This research presents a new technique for electrical fire detection in Internet of Things (IoT) networks using predictive analytics, utilizing the Random Forest algorithm. To proactively detect and reduce fire hazards before they become catastrophic by using the abundance of data produced by IoT sensors. Data such as current measurements, temperature, humidity, and other relevant features may be retrieved in real-time via IoT devices that are integrated into electrical systems according to the suggested system. Random Forest classifier is taught to interpret and classified patterns that suggest potential fire dangers using data preparation, feature engineering, and supervised learning methods. Standard validation measures are used to verify the model's performance, providing its dependability and robustness. IoT network infrastructure deployment of the trained model allows for early identification of possible fire hazards and continuous monitoring. The system's capabilities can be including more sensors and advanced machine learning algorithms. By automating the monitoring process and enabling early detection, the suggested solution increases efficiency and safer and more robust IoT environment. © 2024 ieee.

关键词： Predictive analytics

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Improved Hierarchical Bayesian Optimization Algorithm with LSTM for Crude Oil Price Forecasting 3

Improved Hierarchical Bayesian Optimization Algorithm with L...

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3rd ieee international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Habelalmateen, Mohammed I. Singh, Murlidhar Prasad Chaithra, K.N. Bala Dhandayuthapani, V. Irfan, Md. Mujahid College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq B. P. Mandal College Of Engineering Department Of C.S.& E. Bihar Madhepura India Nitte Meenakshi Institute Of Technology Dept.of Ece Bengaluru India College Of Computing And Information Sciences University Of Technology And Applied Sciences Department Of It Shinas campus Shinas Oman Sr University Department Of Electrical And Electronics Engineering Warangal India

ISBN: (纸本)9798350318609

Accurate oil price forecasting enables economists, entrepreneurs, and policymakers to foresee market trends and make well-informed decisions. The issue statement of current approaches is unable to handle stopping parameter changes, overfitting, underfitting, and the model's learning rate. More data that has been tested and trained upon is present in the West Texas Intermediate (WTI) dataset utilized in the suggested methodology. The suggested technique uses the Long Short-Term Memory Algorithm (LSTM) to forecast the price of crude oil in the future and the Improved Hierarchical Bayesian Optimization Algorithm for hyperparameter tuning. Initially, the raw data is normalized using Variational Mode Decomposition (VMD), representative nonlinear feature information is extracted using Phase Space Reconstruction (PSR), and the chaotic character of the data is confirmed using the maximal Lyapunov exponent. This approach's numerical validation yielded a Coefficient of Determination (R2) of 1.79566, a Mean Square Error (MSE) of 0.00010, a Mean Absolute Error (MAE) of 0.00720, and a Root Mean Square Error (RMSE) of 0.01075. The suggested method outperforms all the current methods, as demonstrated by a comparison of LSTM Approach Tuned by Salp Swarm Algorithm (V-LSTM-SSA-DO) and Forecasting Crude Oil Prices with a WT-FNN Model (WT-FNN). © 2024 ieee.

关键词： Forecasting

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