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Secure Key Management Based Mobile Authentication in Cloud

computer Systems science & engineering 2022年第12期43卷 887-896页

作者： S.Shanmuga Priya D.Yuvaraj T.Satyanarayana Murthy Varghese S.Chooralil S.Navaneetha Krishnan P.Banumathy P.SundaraVadivel Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyTamilnaduIndia Department of Computer Science and Engineering Cihan UniversityDuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering CollegeBapatlaAndhra PradeshIndia Department of Computer Science and Engineering Rajagiri School of Engineering and TechnologyKakkanadKeralaIndia Department Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiTamilnaduIndia Department of Information Technology Karaikal Polytechnic CollegeVarichikudy-KaraikalIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia

Authentication is important to the security of user data in a mobilecloud environment. Because of the server’s user credentials, it is subject toattacks. To maintain data authentication, a novel authentication mechanism is proposed. It consists of three independent phases: Registration, login, and authentication and key agreement. The user registers with the Registration Center (RC)by producing a secret number that isn’t stored in the phone, which protects againstprivileged insider attacks. The user and server generate a nonce for dynamic useridentity and agree on a session secret key for safe communication. The passwordsare not stored on the computer or provided in plain text, they are resistant toreplay, guessing, and stolen verification attacks. The suggested protocol uses aone-way hash function and XOR operations, with the client having remote accessto a large number of servers over a secure communication channel. Concentrateson HMAC and SHA3 for Collision Free Hashing and to overcome length extension attacks. HMACs are substantially less affected by collisions than their underlying hashing algorithms alone. So adding an HMAC to an MD5 or SHA hashwould make it substantially more difficult to break via a rainbow table.

关键词： Registration center SIM IMEI

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Efficient Radon Levels Detection in Residential Areas using Cloud-Enabled Decision Trees for Real-Time Analysis 8

Efficient Radon Levels Detection in Residential Areas using ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lenin, J. Rohit, V.Robin Nisha, F. Vasudevan, B. Sivakumar, S. Murugan, S. Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Bangalore India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Bengaluru India Cmr University School of Engineering and Technology Computer Science & Engineering Bangalore India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur SRM Nagar Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

This research introduces a new approach to radon detection in homes utilizing Decision Trees (DTs) enabled by the cloud in real-time. High radon levels, a natural radioactive gas, are dangerous to human health. Quick evaluation and mitigation efforts are hindered by the long processes required by traditional detection systems. Using DTs, a machine learning methodology, the proposed method sorts of radon levels according to variables, including building materials, ventilation systems, and geographic location. These trees may handle both numerical and categorical data and provide interpretability. Fast processing of massive data sets and real-time analysis across varied residential areas are made possible by integrating cloud computing, increasing efficiency and scalability. Stakeholders may remotely access crucial information due to the solution's accessibility and flexibility. Results from controlled experiments demonstrate that this method can accurately identify radon levels in the air in real time, allowing for preventative actions to lessen potential health hazards. People living in areas prone to radon may benefit from this practice in terms of their health. © 2024 IEEE.

关键词： Indoor air pollution

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Contrastive Learning-based Meta-Learning Sequential Recommendation 2

Contrastive Learning-based Meta-Learning Sequential Recommen...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Li, Naihao Wang, Jiaqi Liu, Xu Wang, Lanfeng Zhang, Long College of Computer and Information Engineering Tianjin Normal University Tianjin China College of Computer and Information Engineering Guangzhou Huali Science and Technology Vocational College Guangdong Guangzhou China

ISBN: (纸本)9798350382952

Contrastive Learning (CL) has significant practical value in the current field of recommendation systems. Contrastive learning involves learning representations by comparing and contrasting the similarities and differences between different data points or samples. Currently, contrastive learning (CL) is emerging as a promising alternative method to tackle challenges of data with low density and high levels of noise in recommendation systems. However, existing CL methods often focus on finding different augmentation operations for different datasets, which leads to a lack of generalization in the models. Additionally, these CL methods may exhibit abnormal behavior when dealing with limited resources due to the uncertainty introduced by data augmentation. Moreover, in recommendation systems, there is a well-known phenomenon where a small number of popular items account for a large portion of user interactions, while numerous cold-start items have extremely few interaction data points. Therefore, this paper proposes a more versatile contrastive learning model that mitigates the issues of data sparsity and noise, termed the Contrastive Learning with Meta-learning Framework (CLMLRec). Building upon the existing model MCLRec, this paper introduces a pre-training warm-up framework (PRWF) that includes Meta Scaling and Shift Networks, which create adjustment and displacement individually for each item. Through this framework, the paper aims to learn to pre-activate cold item embeddings and demonstrates the effectiveness of CLMLRec on commonly used datasets. © 2024 IEEE.

关键词： Recommender systems

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Digital Twin-based Dynamic Resource Provisioning Using Deep Q-Network on 6G-enabled Mobile Edge Networks 17

Digital Twin-based Dynamic Resource Provisioning Using Deep ...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Alex, Suja A. Singh, Neha Adhikari, Mainak St. Xavier's Catholic College of Engineering Department of Information Technology Nagercoil India Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Science Education and Research Thiruvananthapuram School of Data Science Thiruvananthapuram India

ISBN: (纸本)9798331531195

The advancement of Digital Twin (DT) technology has enabled the creation of digital replicas of physical entitics, significantly enhancing the functionality and performance of mobile networks. However, this innovative approach also introduces considerable overload and performance challenges for mobile edge networks. To address these issucs, we propose a novel DT-enabled edge architecture for 6G-capable technology, employing a dynamic resource allocation strategy. The proposed approach, termed DT-based Dynamic Resource Provisioning (DT-DRP), leverages the capabilities of DT technology and Decp Reinforcement Learning (DRL) to dynamically allocate incoming tasks from mobile devices to edge devices. An intelligent DT server facilitates this allocation, optimizing resource use and improving network performance. The predictive capabilities of the framework are validated using the K-Nearest Neighbor (KNN) dlassifier at the edge network. Experimental results demonstrate that the proposed DT-DRP framework incorporates mobile devices into the DT network scamlessly, achieving superior performance in terms of minimum latency and high accuracy compared to bascline Q-learning algorithms across two medical datasets and various exccution scenarios. This research underscores the potential of DT technology in 6G networks and highlights its efficacy in enhancing mobile edge computing performance through intelligent resource management. © 2025 IEEE.

关键词： Mobile edge computing

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Machine Learning-Based Framework for Cybersecurity of Robotic System 6th

Machine Learning-Based Framework for Cybersecurity of Roboti...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Verma, Narinder Kumar, Neerendra Salotra, Harish Department of Computer Science and Information Technology Central University of Jammu Jammu181143 India

ISBN: (纸本)9789819778614

Robotic systems are increasingly integrated into sectors ranging from manufacturing to health care, presenting significant cybersecurity challenges. Ensuring the security and integrity of these systems is critical to prevent malicious attacks that could compromise functionality and safety. This study introduces a machine learning-based framework designed to enhance the cybersecurity of industrial robotic systems. Leveraging the CICIDS 2017 dataset, which provides a comprehensive collection of network traffic data representing modern cyber threats, the framework demonstrates high efficacy in detecting and mitigating such threats. Experimental results show that the K-Nearest Neighbors and Decision Tree algorithms achieve accuracy rates of 99.01% and 99.83%, respectively, underscoring the potential of these techniques for real-time intrusion detection. These findings highlight the framework's effectiveness in protecting robotic systems against cyber-attacks, thereby fortifying their security in various application domains. The findings highlight the framework effectiveness in protecting robotic systems against cyber-attacks, thereby fortifying their security in various application domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyber attacks

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The Future Era of Computing: Automatic Computing

The Future Era of Computing: Automatic Computing

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Singhal, Swasti Pathak, Nilotpal Kiet Group of Institutions Department of Computer Science and Information Technology Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350322309

The paper aim to describe the futuristic perspective of technology that will govern the aspects of our lives. The computers of today would soon start to become decapacitated as the needs, requirements and our dependency on technology continues to evolve. The study makes an attempt to look for the possible futuristic ideas that would aid this massive technological dependence. The ever-growing technology has proved beneficial to mankind however, we should have a glimpse of the next big thing. With the fall in Moore's law and the rise of quantum computing it has now become a necessity that the new options and opportunities must be examined. The term "quantum computing"refers to a sort of computing whose processes may exploit the processes of quantum physics, such as superposition, interference, and entanglement. Quantum computing is a relatively new field of research. Moore's Law is a model of technical economics that has enabled the information technology sector to roughly double the functionality and performance of digital devices every two years while retaining the same cost, power, and space. This paper is merely a small cog in this long chain of futuristic possibilities. © 2023 IEEE.

关键词： Digital devices

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Leaf Diseases Detection using Android Kotlin App with Deep Learning Algorithm 5

Leaf Diseases Detection using Android Kotlin App with Deep L...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Kori, Daneshwari N. Nikkam, Pushpalatha S. Pujari, Jagadeesh D. Department of Information Science and Engineering SDM College of Engineering and Technology Karnataka Dharwad India

ISBN: (纸本)9798350361155

An Android app is being created using Kotlin to support the agricultural industry. Leaf issues might be brought on by a shortage of nutrients, fungi, diseases, or insects. The most typical bacterial, fungal, and viral diseases. To ddetermine the characteristics of the disease spot quantitatively in accordance with its colour, texture, shape, and other elements. We will find and recognize leaf disease using Deep Learning (DL) approaches. One of the techniques employed in this study to detect leaf disease is deep convolutional neural networks (Deep-CNN) with a pre-trained DL model called ResNet-50. Images are categorized using the TensorFlow image classification model. All of these parameters have been taken into consideration when developing a deep-CNN-based disease detection model for leaves using Keras. The ResNet-50 model then processes the testing data set using the validation parameters it has learned. We may take a leaf image from the camera and choose it from the gallery to use as input for the software, which will then determine what kind of ailment the leaf has as well as whether it is healthy or not. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama G.Jose Moses E.Laxmi Lydia Seifedine Kadry Sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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Construction of Cascaded Deep Neural Network with Optimization-Based Feature Selection in CT Images for Detecting Laryngeal Cancer 5th

Construction of Cascaded Deep Neural Network with Optimizati...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Naga Padmavathi, P. Sree Devi, M. Maheswara Rao, V.V.R. Gopala Gupta, Amara S. A. L. G. Department of Computer Science and Engineering Amrita Sai Institute of Science and Technology AP Paritala India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women AP Bhimavaram India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation AP Vaddeswaram India

ISBN: (纸本)9789819767311

A sin laryngeal carcinoma is the most common kind of head and neck cancer to damage the soft tissues of the larynx. To prevent further medical difficulties and to provide better patient care, early stage laryngeal cancer identification is essential. The primary objective of this research is to provide computer-aided cancer diagnosis driven by deep learning. In order to do this, we create a brand-new Optimization-Based Cascaded Deep Neural Network (OCDNN) that distinguishes between good and unhealthy/cancerous tissues. Additionally, we also provide cascaded patch-based CNN (CP-CNN) picture segmentation. The models are assessed using accepted measures that are unique to the biomedical field. When compared to other models, the performance of our suggested OCDNN architecture was exceptional. Additionally, it fared better than other cutting-edge CNN models applied to medical domain challenges. The findings suggest that the suggested method is quite successful in identifying early indications of laryngeal cancer. The results demonstrate that the suggested strategy may help doctors identify laryngeal carcinoma early and precisely. The suggested OCDNN is discovered to attain 98.4% accuracy, 94% precision, 97.5% recall, and 98% *** paragraph of about 100 words to give a brief introduction to your work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computerized tomography

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Optimizing Content-Based Image Retrieval System Using Convolutional Neural Network Models 8th

Optimizing Content-Based Image Retrieval System Using Convol...

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8th International Conference on information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Kashid, Shraddha S. Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Sule, Bipin Deshpande, Arati V. Salve, Bhausaheb S. Department of Computer Engineering MIT ADT University Pune India Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India Computer Engineering Department Sandip University Mahiravani Trimbak Road Maharashtra Nashik India

ISBN: (纸本)9789819766741

This study focuses on improving "Content-Based Image Retrieval" (CBIR) systems through the utilization of optimized convolutional neural network (CNN) models. Traditionally, CBIR relied on text-based approaches, which proved inefficient in the digital era. In this research, we propose a novel visual content-based retrieval method, leveraging key image features like color, texture, and shape. To expedite the process, we employ pre-trained CNN models, such as the ResNet architecture, trained on extensive datasets like Image-Net. Our experiments, conducted on the "Paris 6K and Oxford 5K datasets, demonstrate the superior performance" of the ResNet model compared to the state-of-the-art AlexNet. This is attributed to ResNet’s ability to reduce training time without sacrificing accuracy. The evaluation, based on average mean precision, yielded promising results of 92.12% for Paris 6K and 84.81% for Oxford 5K, showcasing the efficacy of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural network models

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